Subjects -> TRANSPORTATION (Total: 214 journals)
    - AIR TRANSPORT (9 journals)
    - AUTOMOBILES (26 journals)
    - RAILROADS (10 journals)
    - ROADS AND TRAFFIC (9 journals)
    - SHIPS AND SHIPPING (43 journals)
    - TRANSPORTATION (117 journals)

TRANSPORTATION (117 journals)                     

Showing 1 - 53 of 53 Journals sorted by number of followers
Journal of Navigation     Hybrid Journal   (Followers: 203)
Accident Analysis & Prevention     Hybrid Journal   (Followers: 111)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 38)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 38)
Urban, Planning and Transport Research     Open Access   (Followers: 33)
Transportation     Hybrid Journal   (Followers: 32)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 29)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 29)
Journal of Transport and Land Use     Open Access   (Followers: 28)
Transportation Science     Full-text available via subscription   (Followers: 26)
Journal of Transport Geography     Hybrid Journal   (Followers: 22)
European Transport Research Review     Open Access   (Followers: 22)
Public Transport     Hybrid Journal   (Followers: 18)
Nonlinear Dynamics     Hybrid Journal   (Followers: 18)
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 18)
Cities in the 21st Century     Open Access   (Followers: 17)
Economics of Transportation     Partially Free   (Followers: 16)
Open Journal of Safety Science and Technology     Open Access   (Followers: 16)
Transportation Journal     Full-text available via subscription   (Followers: 16)
Transport     Open Access   (Followers: 16)
Journal of Transportation Technologies     Open Access   (Followers: 13)
IET Electrical Systems in Transportation     Open Access   (Followers: 13)
Case Studies on Transport Policy     Hybrid Journal   (Followers: 13)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 13)
Journal of Supply Chain Management Science (JSCMS)     Open Access   (Followers: 13)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 12)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 12)
Journal of Transport & Health     Hybrid Journal   (Followers: 12)
European Journal of Transport and Infrastructure Research (EJTIR)     Open Access   (Followers: 12)
Journal of Transport History     Hybrid Journal   (Followers: 12)
EURO Journal of Transportation and Logistics     Open Access   (Followers: 12)
Sport, Education and Society     Hybrid Journal   (Followers: 12)
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 11)
IET Intelligent Transport Systems     Open Access   (Followers: 11)
Modern Transportation     Open Access   (Followers: 11)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 11)
Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit     Hybrid Journal   (Followers: 11)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 10)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 10)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 9)
Travel Behaviour and Society     Full-text available via subscription   (Followers: 9)
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 9)
International Journal of Transportation Science and Technology     Open Access   (Followers: 9)
Pervasive and Mobile Computing     Hybrid Journal   (Followers: 8)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 8)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 8)
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 8)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 7)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 7)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 7)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 7)
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
Mobility in History     Full-text available via subscription   (Followers: 7)
Transportation Research Procedia     Open Access   (Followers: 6)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 6)
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 6)
Applied Mobilities     Hybrid Journal   (Followers: 5)
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 5)
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 5)
Logistics & Sustainable Transport     Open Access   (Followers: 4)
Journal of Traffic and Transportation Engineering (English Edition)     Open Access   (Followers: 4)
Transportation Letters : The International Journal of Transportation Research     Hybrid Journal   (Followers: 4)
Transport and Telecommunication     Open Access   (Followers: 4)
Vehicular Communications     Full-text available via subscription   (Followers: 4)
IEEE Open Journal of Intelligent Transportation Systems     Open Access   (Followers: 4)
Research in Transportation Business and Management     Partially Free   (Followers: 4)
Transport Problems     Open Access   (Followers: 4)
Transactions on Transport Sciences     Open Access   (Followers: 4)
World Electric Vehicle Journal     Open Access   (Followers: 3)
Journal of Transportation and Logistics     Open Access   (Followers: 3)
Journal of Public Transportation     Open Access   (Followers: 3)
TRANSPORTES     Open Access   (Followers: 3)
Journal of Transportation Security     Hybrid Journal   (Followers: 3)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 2)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 2)
Sport, Ethics and Philosophy     Hybrid Journal   (Followers: 2)
Streetnotes     Open Access   (Followers: 2)
Journal of Big Data Analytics in Transportation     Hybrid Journal   (Followers: 2)
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 2)
International Journal of Transportation Engineering     Open Access   (Followers: 2)
Transportation Research Interdisciplinary Perspectives     Open Access   (Followers: 2)
Journal of Intelligent and Connected Vehicles     Open Access   (Followers: 1)
Open Transportation Journal     Open Access   (Followers: 1)
eTransportation     Open Access   (Followers: 1)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 1)
Transportation Safety and Environment     Open Access   (Followers: 1)
Danish Journal of Transportation Research / Dansk Tidsskrift for Transportforskning     Open Access   (Followers: 1)
Asian Transport Studies     Open Access   (Followers: 1)
Transportation Engineering     Open Access   (Followers: 1)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 1)
Decision Making : Applications in Management and Engineering     Open Access   (Followers: 1)
Transportation Geotechnics     Full-text available via subscription   (Followers: 1)
Romanian Journal of Transport Infrastructure     Open Access   (Followers: 1)
International Journal of Services Technology and Management     Hybrid Journal   (Followers: 1)
Les Dossiers du Grihl     Open Access   (Followers: 1)
Logistics     Open Access   (Followers: 1)
Synthesis Lectures on Mobile and Pervasive Computing     Full-text available via subscription   (Followers: 1)
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Emission Control Science and Technology     Hybrid Journal   (Followers: 1)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Maritime Transport Research     Open Access  
Communications in Transportation Research     Open Access  
IET Smart Cities     Open Access  
Journal on Vehicle Routing Algorithms     Hybrid Journal  
Transportation in Developing Economies     Hybrid Journal  
Vehicles     Open Access  
Periodica Polytechnica Transportation Engineering     Open Access  
Transportation Systems and Technology     Open Access  
LOGI ? Scientific Journal on Transport and Logistics     Open Access  
Promet : Traffic &Transportation     Open Access  
IFAC-PapersOnLine     Open Access  
Revista Transporte y Territorio     Open Access  
Транспортні системи та технології перевезень     Open Access  
Geosystem Engineering     Hybrid Journal  
Logistique & Management     Hybrid Journal  
IATSS Research     Open Access  
Transport in Porous Media     Hybrid Journal  


Similar Journals
Journal Cover
IEEE Open Journal of Intelligent Transportation Systems
Number of Followers: 4  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2687-7813
Published by IEEE Homepage  [228 journals]
  • [Front cover]

    • Abstract: Presents the front cover for this issue of the publication.
      PubDate: 2022
      Issue No: Vol. 3 (2022)

    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • 2022 Editorial IEEE Open Journal of Intelligent Transportation Systems

    • Authors: Bart Van Arem;Shunsuke Kamijo;
      Abstract: Clearly, the year 2021 was a year in which many of us needed to meet, present, discuss and teach from behind our computer screens. Yet, an increasing number of authors are submitting their work to ‘OJ-ITS’, confirming the interest of the ITS community in a Gold Open Access Journal. The number of submissions grew from 44 in 2019–2020 to more than 100 in 2021, while the number of papers published grew from 20 in 2020 to 35 in 2022. The acceptance rate was stable: 70% in 2020, 68% in 2021. The average times until first and final decision are 54 and 10 days, respectively. We are running around 10 special topics, of which the topic Machine Learning and Deep Learning, led by Dr. Chi-Hua Chen attracted 16 submissions.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • IEEE Open Journal of Intelligent Transportation Systems Instructions for

    • Abstract: Provides instructions and guidelines to prospective authors who wish to submit manuscripts.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Parameter Fuzzy Self-Adaptive Dynamic Window Approach for Local Path
           Planning of Wheeled Robot

    • Authors: Lidan Xiang;Ximin Li;Hao Liu;Peng Li;
      Pages: 1 - 6
      Abstract: As a classic algorithm for realizing robot local path planning, the dynamic window approach (DWA) uses an objective function to choose the optimal velocity commands. However, the path generated by the DWA in the complex environment is not smooth. Therefore, this paper proposes an improved DWA algorithm to make the path of the robot more smoother when avoiding obstacles. At the same time, in view of the condition that the weight coefficients of the evaluation function of the original DWA remain unchanged, this paper will add a fuzzy controller to realize the weight coefficients adaptation, so as to adapt to a more complex environment, and the generated path can be smoother.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Multi-Access Edge Computing-Based Vehicle-Vehicle-RSU Data Offloading Over
           the Multi-RSU-Overlapped Environment

    • Authors: Shih-Yang Lin;Chung-Ming Huang;Tzu-Yu Wu;
      Pages: 7 - 32
      Abstract: This paper proposes a predicted k-hop-limited multi-RSU-considered (PKMR) vehicle to vehicle to roadside unit (RSU) (VVR) data offloading method based on the architecture of the Software Defined Network (SDN) controller inside the multi-access edge computing (MEC) server. In the proposed method, a source vehicle that wants to offload data traffic can use a VVR path that connects the source vehicle and the ahead/rear RSU to perform RSU data offloading when the source vehicle approaches the ahead RSU or leaves the rear RSU. Since some RSUs’ signal ranges may overlap, multi-RSU deployment and RSU handoff between signal-overlapping RSUs must be managed to utilize VVR-based RSU data offloading as much as possible. Based on a vehicle’s periodically reported contexts received by the MEC server, the SDN controller inside the MEC server can execute the proposed PKMR method, which adopts (i) the time-extended prediction mechanism to find the potential VVR paths that exist in a coming time period [tc, tc+T] and (ii) a quality function that takes vehicles’ and RSUs’ network conditions into consideration to select the most suitable VVR data offloading path. The performance evaluation results indicate that the proposed PKMR method produces better data offloading performance than the traditional self-offloading method.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • NAPC: A Neural Algorithm for Automated Passenger Counting in Public
           Transport on a Privacy-Friendly Dataset

    • Authors: Robert Seidel;Nico Jahn;Sambu Seo;Thomas Goerttler;Klaus Obermayer;
      Pages: 33 - 44
      Abstract: Real-time load information in public transport is of high importance for both passengers and service providers. Neural algorithms have shown a high performance on various object counting tasks and play a continually growing methodological role in developing automated passenger counting systems. However, the publication of public-space video footage is often contradicted by legal and ethical considerations to protect the passengers’ privacy. This work proposes an end-to-end Long Short-Term Memory network with a problem-adapted cost function that learned to count boarding and alighting passengers on a publicly available, comprehensive dataset of approx.13,000 manually annotated low-resolution 3D LiDAR video recordings (depth information only) from the doorways of a regional train. These depth recordings do not allow the identification of single individuals. For each door opening phase, the trained models predict the correct passenger count (ranging from 0 to 67) in approx.96% of boarding and alighting, respectively. Repeated training with different training and validation sets confirms the independence of this result from a specific test set.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • A Credibility Assessment Approach for Scenario-Based Virtual Testing of
           Automated Driving Functions

    • Authors: Christoph Stadler;Francesco Montanari;Wojciech Baron;Christoph Sippl;Anatoli Djanatliev;
      Pages: 45 - 60
      Abstract: An immense test space is pushing the development and testing of automated driving functions from real to virtual environments. The virtual world is provided by interconnected simulation models representing sensors, vehicle dynamics, and both static and dynamic environment. For the virtual validation of automated driving, special attention must be paid to the simulation’s credibility, which can be impaired by inappropriate or inaccurate simulation models and tools. Therefore, in this work a method is proposed to assess the credibility of simulation-based testing for automated driving. The approach allows a qualitative and relatively quantitative comparisons between scenarios as well as between different simulation setups. Therefore, several uni- and multivariate metrics are applied towards a scoring of similarity of the behavior between simulation and real test drive. This is achieved by using ground truth data in form of simulation scenarios from real world measurement data. In this way, the virtual automated vehicle encounters the same conditions and surroundings than its counterpart in the real world for evaluating their similarity. The practical applicability of the proposed credibility assessment approach is demonstrated in a case study, in which the credibility of an exemplary simulation-based test bench is inferred.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Countering Adversarial Attacks on Autonomous Vehicles Using Denoising
           Techniques: A Review

    • Authors: A. Kloukiniotis;A. Papandreou;A. Lalos;P. Kapsalas;D.-V. Nguyen;K. Moustakas;
      Pages: 61 - 80
      Abstract: The evolution of automotive technology will eventually permit the automated driving system on the vehicle to handle all circumstances. Human occupants will be just passengers. This poses security issues that need to be addressed. This paper has two aims. The first one investigates strategies for robustifying scene analysis of adversarial road scenes. A taxonomy of the defense mechanisms for countering adversarial perturbations is initially presented, classifying those mechanisms in three major categories: those that modify the data, those that propose adding extra models, and those that focus on modifying the models deployed for scene analysis. Motivated by the limited number of surveys in the first category, we further analyze the approaches that utilize input transformation operations as countermeasures, further classifying them in supervised and unsupervised methods and highlighting both their strengths and weaknesses. The second aim of this paper is to publish CarlaScenes dataset produced using the CARLA simulator. An extensive evaluation study, on CarlaScenes, is performed testing the supervised deep learning approaches that have been either proposed for image restoration or adversarial noise removal. The study presents insights on the robustness of the aforementioned approaches in mitigating adversarial attacks in scene analysis operations.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Super-Twisting Hybrid Control for Ship-Borne PMSM

    • Authors: Di-Fen Shi;Run-Min Hou;Yuan Gao;Xiao-Hui Gu;Yuan-Long Hou;
      Pages: 81 - 88
      Abstract: In the field of ship-borne PMSM, there exists the sea wave fluctuations, external disturbances thus model uncertainties are always challenging the control design of ship-borne permanent magnet synchronous motors (PMSMs). To deal with this problem, a super-twisting extended state observer (SESO) is used in this paper to observe state variables accurately. Moreover, to solve the phase delay problem in active disturbance rejection control (ADRC), Taylor’s formula-based tracking differentiator (TTD) is applied in the proposed hybrid control strategy. With appropriate compensate of disturbance, denoted as super-twisting hybrid control, the controlled position signal can follow the reference with small tracking errors, also with improved dynamic performance. Simulation results show that the proposed super-twisting hybrid control has the better anti-disturbance and tracking performance compared with the traditional ADRC. Lastly, semi-physical experimental results further validate the effectiveness of the control strategy for ship-borne PMSMs.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Automated Quantification of Occupant Posture and Shoulder Belt Fit Using
           Safety Specific Key Points

    • Authors: Franz Hartleitner;A. Koppisetty;K. Bohman;
      Pages: 89 - 103
      Abstract: Virtual evaluation of automotive safety with variation in occupant posture and shoulder belt fit is gaining importance, and there is a need of methods facilitating analysis of occupant postures in driving studies. This study is aimed to develop an AI-based computer vision method to automatically quantify occupant posture and shoulder belt position over time in a car. Traceable defined key points on the occupant were related with the shoulder belt and quantified over time in real 3D coordinates by predefined key measurements, utilising the underlying spatial information of a Intel RealSense 3D Camera. The key points are defined as traceable key points relevant to relate the occupant to the vehicle environment and to estimate shoulder belt position. Key point prediction results suggest an average deviation of around 1cm per coordinate, which enable a reliable spatial categorization of the respective tracked occupant by analyzing the key measurements. This method providing continuous information of the occupant position and belt fit will be useful to identify common occupant postures as well as more extreme postures, to be used for expanding variations in postures for vehicle safety assessments.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Evaluation Model of Ship Berthing Behavior Based on AIS Data

    • Authors: Cheng Fang;Jingang Yin;Hongxiang Ren;
      Pages: 104 - 110
      Abstract: In recent years, the research on ship behavior and ship berthing behavior is a hot topic in the navigation theory research, but the research on ship berthing behavior evaluation is relatively less. Based on the navigation theory, the characteristics of ship berthing behavior (including course, course changes, speed, speed changes and ship position) are extracted, and the berthing behavior evaluation index system is established. According to the characteristics of each behavior, the mathematical description is given for quantitative analysis. Based on the principle of “safety, stability and efficiency”, the evaluation standard is determined for each evaluation index, and the evaluation model is established. The Z-score method is used to standardize the evaluation index data, and the critical method is used to calculate the objective weight of each evaluation index; finally, the model is used to get the evaluation results of each berthing behavior. In this paper, the evaluation model is tested by using the one month’s AIS (Automatic Identification System) berthing data of Tianjin port, and the evaluation results are in line with the actual navigation.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Classification and Evaluation of Driving Behavior Safety Levels: A Driving
           Simulation Study

    • Authors: Kui Yang;Christelle Al Haddad;George Yannis;Constantinos Antoniou;
      Pages: 111 - 125
      Abstract: The road traffic safety situation is severe worldwide and exploring driving behavior is a research hotspot since it is the main factor causing road accidents. However, there are few studies investigating how to evaluate real-time traffic safety of driving behavior and the number of driving behavior safety levels has not yet been thoroughly explored. This paper aims to propose a framework of real-time driving behavior safety level classification and evaluation, which was validated by a case study of driving simulation experiments. The proposed methodology focuses on determining the optimal aggregation time interval, finding the optimal number of safety levels for driving behavior, classifying the safety levels, and evaluating the driving safety levels in real time. An improved cross-validation mean square error model based on driver behavior vectors was proposed to determine the optimal aggregation time interval, which was found to be 1s. Three clustering techniques were applied, i.e., k-means clustering, hierarchical clustering and model-based clustering. The optimal number of clusters was found to be three. Support vector machines, decision trees and naïve Bayes classifiers were then developed as classification models. The accuracy of the combination of k-means clustering and decision trees proved to be the best with three clusters.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Multi-Intersection Traffic Optimisation: A Benchmark Dataset and a Strong

    • Authors: Hu Wang;Hao Chen;Qi Wu;Congbo Ma;Yidong Li;
      Pages: 126 - 136
      Abstract: The control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. However, it is challenging since traffic dynamics are complicated in real-world scenarios. Because of the high complexity of the optimisation problem for modeling the traffic, experimental settings of existing works are often inconsistent. Moreover, it is not trivial to control multiple intersections properly in real complex traffic scenarios due to its vast state and action space. Failing to take intersection topology relations into account also results in inferior solutions. To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios. Additionally, we propose a novel baseline model with strong performance. It is based on deep reinforcement learning with an encoder-decoder structure: an edge-weighted graph convolutional encoder to excavate multi-intersection relations; and an unified structure decoder to jointly model multiple junctions in a comprehensive manner, which significantly reduces the number of the model parameters. By doing so, the proposed model is able to effectively deal with the multi-intersection traffic control optimisation problem. Models are trained/tested on both synthetic and real maps and traffic data with the Simulation of Urban Mobility (SUMO) simulator. Experimental results show that the proposed model surpasses multiple competitive methods. The traffic data and the code can be found at
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • The Impact of Potentially Realistic Fabricated Road Sign Messages on Route

    • Authors: Alireza Ermagun;Kaveh Bakhsh Kelarestaghi;Kevin Heaslip;
      Pages: 137 - 145
      Abstract: This article studies self-reported route change behavior of 4,706 licensed drivers in the continental U.S. through a stated preference survey when they encounter road sign messages. Respondents are asked to score their likelihood of route change and speed change on a 5-point Likert scale to three messages: (1) “Heavy Traffic Due to Accident,” (2) “Road Closure Due to Police Activity,” and (3) “Storm Watch, Flooding in Area Soon.” We fulfill three objectives. First, we identify the relationship between the route change behavior and socioeconomic and attitudinal-related factors. Second, we explore the impact of road sign messages with different contents on route change behavior. Third, we test the association between route change and speed change behaviors. The results demonstrate that: (1) the response of participants to compromised dynamic message signs varies according to the socioeconomic standing and attitude of participants, (2) the response of participants varies under different messages, and socioeconomic and attitudinal factors impact this differentiation, and (3) the likelihood of route change is positively associated with slowing down. This means, in practice, a malicious adversary has the potential to shunt and disturb traffic by disseminating fabricated messages and engineering route choice of drivers.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • VHF Speech Enhancement Based on Transformer

    • Authors: Xue Han;Mingyang Pan;Zhengzhong Li;Haipeng Ge;Zongying Liu;
      Pages: 146 - 152
      Abstract: To solve the poor quality of Very high frequency (VHF) speech communication in the navigation field, a VHF speech enhancement model based on an improved transformer (VHFSE) is proposed in this paper. The long-term and short-term noise are the reasons for the poor quality of VHF voice communication. VHFSE can reduce these two aspects of noise. We select the Two-stage Transformer based Neural Network (TSTNN) as the baseline. The Transformer structure pays attention to global information and parallel computing, which can reduce the long-term noise. In order to strengthen the ability of the model to reduce short-term noise, we add CNN module to the transformer according to the ability of revolutionary neural networks (CNN) to extract local information. Meanwhile, to improve the real-time performance, this study employs the lightweight convolution module (Depthwise Separable Convolution) to efficiency of VHF speech communication. Experimental results show that the proposed model VHFSE obtains the highest PESQ and STOI values than other compared modules. Besides, we apply the self-built dataset in our proposed model. The spectrum diagram shows that our model has the best enhancement effect on navigation VHF speech.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • A Collaborative Berth Planning Approach for Disruption Recovery

    • Authors: Xiaohuan Lyu;Rudy R. Negenborn;Xiaoning Shi;Frederik Schulte;
      Pages: 153 - 164
      Abstract: Traditionally, terminal operators create an initial berthing plan before the arrival of incoming vessels. This plan involves decisions on when and where to load or discharge containers for the calling vessels. However, disruptive unforeseen events (i.e., arrival delays, equipment breakdowns, tides, or extreme weather) interfere with the implementation of this initial plan. For terminals, berths and quay cranes are both crucial resources, and their capacity limits the efficiency of port operations. Thus, one way to minimize the adverse effects caused by disruption is to ally different terminals to share berthing resources. In some challenging situations, terminal operators also need to consider the extensive transshipment connections between feeder and mother vessels. Therefore, in this work, we investigate a collaborative variant of the berth allocation recovery problem which focuses on the collaboration among terminals and transshipment connections between vessels. We propose a mixed-integer programming model to (re)-optimize the initial berth and quay crane allocation plan and develop a Squeaky Wheel Optimization metaheuristic to find near-optimal solutions for large-scale instances. The results from the performed computational experiments, considering multiple scenarios with disruptive events, show consistent improvements of up to 40% for the suggested collaborative strategy (in terms of costs for the terminal operators).
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • A Real-Time Safety-Based Optimal Velocity Model

    • Authors: Awad Abdelhalim;Montasir Abbas;
      Pages: 165 - 175
      Abstract: Modeling safety-critical driver behavior at signalized intersections needs to account for the driver’s planned decision process, where a driver executes a plan to avoid collision in multiple time steps. Such a process can be embedded in the Optimal Velocity Model (OVM) that traditionally assumes that drivers base their “mental intention” on a distance gap only. We propose and evaluate a data-driven OVM based on real-time inference of roadside traffic video data. First, we extract vehicle trajectory data from roadside traffic footage through our advanced video processing algorithm (VT-Lane) for a study site in Blacksburg, VA, USA. Vehicles engaged in car-following episodes are then identified within the extracted vehicle trajectories database, and the real-time time-to-collision (TTC) is calculated for all car-following instances. Then, we analyze the driver behavior to predict the shape of the underlying TTC-based desired velocity function. A clustering approach is used to assess car-following behavior heterogeneity and understand the reasons behind outlying driving behaviors at the intersection to design our model accordingly. The results of this assessment show that the calibrated TTC-based OVM can replicate the observed driving behavior by capturing the acceleration pattern with an error 20% lower than the gap distance-based OVM.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Real-Time Pedestrian Conflict Prediction Model at the Signal Cycle Level
           Using Machine Learning Models

    • Authors: Shile Zhang;Mohamed Abdel-Aty;
      Pages: 176 - 186
      Abstract: Compared with traditional traffic studies, real-time safety analyses can be better incorporated into proactive traffic management strategies to improve traffic safety. However, few studies have investigated the real-time pedestrian safety model. Intersections usually have mixed traffic conditions with more pedestrian-vehicle interactions. This paper uses conflict indicators, PET (Post Encroachment Time) and TTC (Time to Collision) to identify pedestrians’ conflicts from CCTV (closed-circuit television) videos. The high-resolution traffic data from the Automated Traffic Signal Performance Measures (ATSPM) system are used to derive traffic flow-related variables. The pedestrian exposure is also estimated. Pedestrians’ conflicts are predicted using multiple machine learning models and Logistic Regression. The resampling methods, random over-sampling, and random under-sampling are compared. The best model, Extreme Gradient Boosting (XGBT) with random over-sampling method can achieve AUC (area under the ROC curve) value of 0.841 and recall value of 0.739 on the test data set. The proposed model can predict pedestrians’ conflicts one cycle ahead, which can be 2–3 min. The proposed model has the potential to be implemented in the Connected and Automated Vehicles (CAV) environment to adjust signal timing accordingly and enhance traffic safety.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Reinforcement Learning-Based Traffic Control: Mitigating the Adverse
           Impacts of Control Transitions

    • Authors: Robert Alms;Aristeidis Noulis;Evangelos Mintsis;Leonhard Lücken;Peter Wagner;
      Pages: 187 - 198
      Abstract: An important aspect of automated driving is to handle situations where it fails or is not allowed in specific traffic situations. This case study explores means, by which control transitions in a mixed autonomy system can be organized in order to minimize their adverse impact on traffic flow. We assess a number of different approaches for a coordinated management of transitions, covering classic traffic management paradigms and AI-driven controls. We demonstrate that they yield excellent results when compared to a do-nothing scenario. This text further details a model for control transitions that is the basis for the simulation study presented. The results encourage the deployment of reinforcement learning on the control problem for a scenario with mandatory take-over requests.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Fatality Prediction for Motor Vehicle Collisions: Mining Big Data Using
           Deep Learning and Ensemble Methods

    • Authors: Mahzabeen Emu;Farjana Bintay Kamal;Salimur Choudhury;Quazi Abidur Rahman;
      Pages: 199 - 209
      Abstract: Motor vehicle crashes are one of the most common causes of fatalities on the roads. Real-time severity prediction of such crashes may contribute towards reducing the rate of fatality. In this study, the fundamental goal is to develop machine learning models that predict whether the outcome of a collision will be fatal or not. A Canadian road crash dataset containing 5.8 million records is utilized in this research. In this study, ensemble models have been developed using majority and soft voting to address the class imbalance in the dataset. The prediction accuracy of approximately 75% is achieved using Convolutional Neural Networks. Moreover, a comprehensive analysis of the attributes that are important in distinguishing between fatal vs. non-fatal motor vehicle collisions has been presented in this paper. In-depth information content analysis reveals the factors that contribute the most in the prediction model. These include roadway characteristics and weather conditions at the time of the crash, vehicle type, time when the collision happen, road user class and their position, any safety device used, and the status of traffic control. With real-time data based on weather and road conditions, an automated warning system can potentially be developed utilizing the prediction model employed in this study.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • How and Why Freight Trains Deviate From the Timetable: Evidence From

    • Authors: Carl-William Palmqvist;Anne Lind;Victoria Ahlqvist;
      Pages: 210 - 221
      Abstract: European infrastructure managers (IMs) create annual timetables for trains that will run during a year. Freight trains in Sweden often deviate from this by being added, canceled, delayed or early, resulting in increased costs for IMs and railway undertakings (RUs). We investigate the frequency of and causes for these deviations, using one year of operational data for 48,000 trains, and 15 stakeholder interviews. We find that about 20% of freight trains are added once the timetable has been created, and that cancelations occur for about 35% of freight trains, mostly at the RUs’ initiative. Delays are common: some 40% of departures, 30% of runtimes, and 20% of dwell times are delayed. Running early is even more common: 80% are ready to depart early, and 60% do so, while 40% of runtimes and 75% of dwell times are shorter than scheduled. We find links and feedback loops between the root causes for these deviations and suggest that IMs reserve more of the capacity that is needed for freight trains and instead distribute it throughout the year. This could lead to more appropriate, attractive, and reliable timetables for freight trains, whilst greatly reducing the amount of planning effort.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • LiCaNet: Further Enhancement of Joint Perception and Motion Prediction
           Based on Multi-Modal Fusion

    • Authors: Yasser H. Khalil;Hussein T. Mouftah;
      Pages: 222 - 235
      Abstract: The safety and reliability of autonomous driving pivots on the accuracy of perception and motion prediction pipelines, which reckons primarily on the sensors deployed onboard. Slight confusion in perception and motion prediction can result in catastrophic consequences due to misinterpretation in later pipelines. Therefore, researchers have recently devoted considerable effort towards enhancing perception and motion prediction models. However, targeting pixel-wise joint perception and motion prediction using different sensor modalities are often ignored. In this paper, we push performance even further by leveraging a multi-modal fusion network. We propose a novel LIDAR Camera Network (LiCaNet) that achieves accurate pixel-wise joint perception and motion prediction in real-time. LiCaNet expands on our earlier fusion network by incorporating a camera image into the fusion of LIDAR sourced sequential bird’s-eye view (BEV) and range view (RV) images. We present a comprehensive evaluation using nuScenes dataset to validate the outstanding performance of LiCaNet compared to the state-of-the-art. Experiments reveal that utilizing a camera sensor results in a substantial gain in perception and motion prediction. Moreover, most of the improvements achieved fall within the camera range, with the highest registered for small and distant objects, confirming the significance of incorporating a camera sensor into a fusion network.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Wireless-Signal-Based Vehicle Counting and Classification in Different
           Road Environments

    • Authors: Raoul Kanschat;Shivam Gupta;Auriol Degbelo;
      Pages: 236 - 250
      Abstract: Traffic monitoring is key to modern city planning. However, the costs associated with monitoring devices limit the large-scale deployment of existing traffic monitoring systems. In this article, we propose and evaluate an algorithm to automatically count the number of vehicles that have passed through a low-cost system for traffic monitoring. The system uses deviations in the Wi-Fi signals strength to predict the presence of a vehicle on the road and its type (car, bus). The study further systematically compares six analytical techniques for the classification of detected vehicles. The methods were tested with data from three road scenarios in the city of Münster, Germany. Vehicle classification accuracy ranged from 83% up to 100% in our study. We also observed that a higher Wi-Fi frequency (5 GHz) was superior to the 2.4 GHz for improving the overall vehicle detection and the results of the classification algorithms. The results suggest that the Wi-Fi-based techniques proposed in this study are promising for cost-efficient traffic monitoring in cities in a privacy-preserving manner.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Data-Driven Vehicle Rebalancing With Predictive Prescriptions in the
           Ride-Hailing System

    • Authors: Xiaotong Guo;Qingyi Wang;Jinhua Zhao;
      Pages: 251 - 266
      Abstract: Rebalancing vacant vehicles is one of the most critical strategies in ride-hailing operations. An effective rebalancing strategy can significantly reduce empty miles traveled and reduce customer wait times by better matching supply and demand. While the supply (vehicles) is usually known to the system, future passenger demand is uncertain. There are two ways to handle uncertainty. First, the point-prediction-driven optimization framework involves predicting the future demand and then producing rebalancing decisions based on the predicted demand. Second, the data-driven optimization approaches directly prescribe rebalancing decisions from data. In this study, a predictive prescription framework is introduced to this problem, where the benefits of predictive and data-driven optimization models are combined. Based on a state-of-the-art vehicle rebalancing model, the matching-integrated vehicle rebalancing (MIVR) model, predictive prescriptions are introduced to handle demand uncertainty. Model performances are evaluated using real-world simulations with New York City (NYC) ride-hailing data under four demand scenarios. When demand can be accurately predicted, a point-prediction-driven optimization framework should be adapted. The proposed predictive prescription models achieve shorter customer wait times over the point-prediction-driven optimization models when future demand predictions are not so accurate, and achieve a competitive performance with respect to the cutting-edge robust optimization models.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Prediction of Queue Dissipation Time for Mixed Traffic Flows With Deep

    • Authors: Hung-Hsun Chen;Yi-Bing Lin;I-Hau Yeh;Hsun-Jung Cho;Yi-Jung Wu;
      Pages: 267 - 277
      Abstract: Queue dissipation has been extensively studied about traffic signalization, work zone operations, and ramp metering. Various methods for estimating the intersection’s queue length and dissipation time have been reported in the literature, including the use of car-following models with simulation, vehicle trajectories from GPS, shock-wave theory, statistical estimation from traffic flow patterns, and artificial neural networks (ANN). However, most of such methods cannot account for the impacts of interactions between different vehicle types and their spatial distributions in the queue length on the initial discharge time and the resulting total dissipation duration. As such, this study presents a system, named TrafficTalk, that applies a deep learning-based method to reliably capture the queue characteristics of mixed traffic flows, and produce a robust estimate of the dissipating duration for the design of the optimal signal plan. The proposed TrafficTalk, featuring the effectiveness in transforming video-imaged traffic conditions into vehicle density maps, has proved its performance under extensive field evaluations. For instance, compared with the benchmark model, XGBoost in the literature, it has reduced the MAPE from 25.8% to 10.4%., and from 31.3% to 10.4% if the queue discharging stream comprises motorcycles.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • A Vehicular Crowdsensing Market for AVs

    • Authors: Alireza Chakeri;Xin Wang;Luis G. Jaimes;
      Pages: 278 - 287
      Abstract: The rapid adoption of the vehicles and their on-board sensors as a primary means of transportation make them natural candidates for the outsourcing of data collection. However, vehicles mobility patterns tend to cluster into specific regions such as highways and popular roads, that makes their utilization difficult for data collection in isolated regions with low density traffic. We tackle this problem by proposing a probabilistic incentive mechanism for Vehicular Crowdsensing (VCS) that encourages vehicles to deviate from their pre-planned trajectories in order to visit and collect data from the isolated places. Our proposed framework is able to handle asynchronous vehicles. Also, vehicles consider the traffic holistically to find more profitable routes. By using a realistic vehicular movement data set (UBER movement), open-street maps (OSM) and SUMO vehicular traffic simulator, we show our algorithm significantly outperforms traditional approaches for trajectory generation in terms of spatial and temporal coverage, road utilization, and average participant utility.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Quantitative Evaluation of the Impacts of the Time Headway of Adaptive
           Cruise Control Systems on Congested Urban Freeways Using Different Car
           Following Models and Early Control Results

    • Authors: Lina Elmorshedy;Baher Abdulhai;Islam Kamel;
      Pages: 288 - 301
      Abstract: The impact of driving automation and adaptive cruise control (ACC) on traffic performance has been increasingly studied in recent years. This paper focuses on two widely used ACC car following models and investigates the impact of the time headway parameter on traffic operation and performance on one of the busiest freeway corridors in Ontario, Canada. Using Aimsun microsimulation, we compare two commonly used ACC car following models; the intelligent driver model (IDM) and Shladover’s model which has been recently adopted in Aimsun Next 20. Several experiments have been conducted to evaluate the freeway performance for different desired headway settings and market penetration rates of ACC-equipped vehicles. Simulations results confirm the reported IDM drawbacks of having a slow response leading to headway errors which are less pronounced with Shladover’s model thereby leading to more accurate quantification by the latter. This study further presents a simple on-off ACC-based traffic control strategy which aims to adapt in real time the driving behavior of ACC-equipped vehicles to the prevailing traffic conditions so that freeway performance is improved. The simulation results demonstrate that, even for low penetration rates of ACC vehicles, the proposed control concept improves the average network throughput, delay, and speed compared to the case of only manually driven or uncontrolled ACC vehicles.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • A Nationwide Impact Assessment of Automated Driving Systems on Traffic
           Safety Using Multiagent Traffic Simulations

    • Authors: Sou Kitajima;Hanna Chouchane;Jacobo Antona-Makoshi;Nobuyuki Uchida;Jun Tajima;
      Pages: 302 - 312
      Abstract: The objective of this paper is to propose a methodology to estimate nationwide traffic safety impacts of automated vehicle technologies using multi-agent traffic simulations. The influence of three levels of driver trust in the automation system (appropriate, over trust, distrust) is considered in the simulation and takes different transition modes of control between the driver and the system into account. The nationwide estimation of crashes is obtained by projecting results of the simulations using traffic data for three different and representative municipalities. Results indicated that Automated Driving Systems and Advanced Driver Assistance Systems significantly reduced the number of casualties and fatalities compared to manual driving. Simulation results in consideration of the influence of driver trust also found that this reduction may be negatively affected by over- and under-trust parameters. However, even with the introduction of these parameters, the reduction rate was still significant compared to manual driving. The proposed methodology using multi-agent traffic simulations may thus address concerns surrounding the deployment of automated driving systems which is a feature not found in conventional simulations, provide useful insight for interested parties to develop research and policy making strategies that accelerate traffic safety improvements, and to support social acceptance efforts.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Designing Lookahead Policies for Sequential Decision Problems in
           Transportation and Logistics

    • Authors: Warren B. Powell;
      Pages: 313 - 327
      Abstract: There is a wide range of sequential decision problems in transportation and logistics that require dealing with uncertainty. There are four classes of policies that we can draw on for different types of decisions, but many problems in transportation and logistics will ultimately require some form of direct lookahead policy (DLA) where we optimize decisions over some horizon to make a decision now. The most common strategy is to use a deterministic lookahead (think Google maps), but what if you want to handle uncertainty' In this paper, we identify two major strategies for designing practical, implementable lookahead policies which handle uncertainty in fundamentally different ways. The first is a suitably parameterized deterministic lookahead, where the parameterization is tuned in a stochastic simulator. The second uses an approximate stochastic lookahead, where we identify six classes of approximations, one of which involves designing a “policy-within-a-policy,” for which we turn to all four classes of policies. We claim that our approximate lookahead model spans all the classical stochastic optimization tools for lookahead policies, while opening up pathways for new policies. But we also insist that the idea of a parameterized deterministic lookahead is a powerful new idea that offers features that, for some problems, can outperform the more familiar stochastic lookahead policies.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • A Data-Driven Model for Pedestrian Behavior Classification and Trajectory

    • Authors: Vasileia Papathanasopoulou;Ioanna Spyropoulou;Harris Perakis;Vassilis Gikas;Eleni Andrikopoulou;
      Pages: 328 - 339
      Abstract: Pedestrian modeling remains a formidable challenge in transportation science due to the complicated nature of pedestrian behavior and the irregular movement patterns. To this extent, accurate and reliable positioning technologies and techniques play a significant role in the pedestrian simulation studies. The objective of this research is to predict pedestrian movement in various perspectives utilizing historical trajectory data. The study features considered in this research are pedestrian class, speed and position. The ensemble of these features provides a thorough description of pedestrian movement prediction, whilst contributes to the context of pedestrian modeling and Intelligent Transportation Systems. More specifically, pedestrian movement is grouped into different classes considering gender, walking pace and distraction by employing random forest algorithms. Then, position and speed prediction is computed employing suitable data-driven methods, in particular, the locally weighted regression (LOESS method), taking into account the individual pedestrian’s profile. An LSTM-based (Long Short-Term Memory) model is also applied for comparison. The methodology is applied on pedestrian trajectory data that were collected in a controlled experiment undertaken at the Campus of the National Technical University of Athens (NTUA), Greece. Prediction of pedestrian’s movement is achieved, yielding satisfactory results.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Cascaded Feature-Mask Fusion for Foreground Segmentation

    • Authors: Chuanyun Xu;Huan Liu;Tenghui Li;Yang Zhang;Tian Li;Gang Li;
      Pages: 340 - 350
      Abstract: Foreground segmentation aims at extracting moving objects from the background in a robust manner under various challenging scenarios. The deep learning-based methods have achieved remarkable improvement in this field. These methods produce semantically correct predictions based on extracted rich semantic features yet perform poorly on segmentation of edge details. The main reason is that the high-level features extracted by the deep network lose the high-frequency information for the successful edge segmentation. On this basis, we propose a novel segmentation network with a cascade architecture to refine segmentation results step by step by introducing detailed information into high-level features. The network recorrects and optimizes the segmentation maps in each step so that more accurate segmentation results are obtained. Furthermore, we evaluate our approach on the challenging CDnet2014 dataset and achieve an F-measure of 0.9868. Our approach thus outperforms previous methods, such as FgSegNet_v2, FgSegNet, BSPVGan, Cascade CNN, IUTIS-5, WeSamBE, DeepBS, and GMM-Stauffer.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Generalized Path Planning for UTM Systems With a Space-Time Graph

    • Authors: Rafael Papa;Ionut Cardei;Mihaela Cardei;
      Pages: 351 - 368
      Abstract: Motivated by the increased use of UAS in commercial applications, in this paper we tackle the problem of path planning when requests are submitted by UAS managed by different operators. We propose the new problem of generalized path planning for UAS Traffic Management, where the UAS path is described by operators with a sequence of waypoint groups and a solution trajectory must pass through a waypoint in each group. This problem is typical for applications where multiple charging stations and pickup/drop-off locations are distributed in a flight area. Our solution builds upon prior work on discretized space-time graph path planning and proposes a novel multi-source/multi-destination graph search algorithm that generates collision-free trajectories for pre-flight CDR. Our efficient algorithm has runtime proportional to the number of groups and avoids combinatorial explosion. We apply our mechanism to the energy-constrained UAS package delivery problem with multiple warehouses and battery charging stations. Simulation results show that our algorithm is efficient and scalable with the number of requests and graph size. The addition of charging stations and the option for multiple warehouses increases the request admission ratio and reduces the overall trajectory duration, effectively improving both the planner’s quality of service and the efficiency of airspace usage.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • ITANS: Incremental Task and Network Scheduling for Time-Sensitive Networks

    • Authors: Anna Arestova;Wojciech Baron;Kai-Steffen J. Hielscher;Reinhard German;
      Pages: 369 - 387
      Abstract: Recent trends such as automated driving in the automotive field and digitization in factory automation confront designers of real-time systems with new challenges. These challenges have arisen due to the increasing amount of data and an intensified interconnection of functions. For distributed safety-critical systems, this progression has the impact that the complexity of scheduling tasks with precedence constraints organized in so-called cause-effect chains increases the more data has to be exchanged between tasks and the more functions are involved. Especially when data has to be transmitted over an Ethernet-based communication network, the coordination between the tasks running on different end-devices and the network flows has to be ensured to meet strict end-to-end deadlines. In this work, we present an incremental heuristic approach that computes schedules for distributed and data-dependent cause-effect chains consisting of multi-rate tasks and network flows in time-sensitive networks. On the one hand, we provide a common task model for tasks and network flows. On the other hand, we introduce the concept of earliest and latest start times to speed up the solution discovery process and to discard infeasible solutions at an early stage. Our algorithm is able to solve large problems for synthetic network topologies with randomized data dependencies in a few seconds on average under strict end-to-end deadlines. We have achieved a high success rate for multi-rate cause-effect chains and an even better result for homogeneous or harmonic chains. Our approach also showed low jitter for homogeonous cause-effect chains.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • A Methodology for Monitoring Rail Punctuality Improvements

    • Authors: C. W. Palmqvist;I. Kristoffersson;
      Pages: 388 - 396
      Abstract: Punctuality is an important aspect of train operations, highly valued by passengers. Both Swedish and Norwegian railways have introduced frameworks to systematically improve punctuality in their systems, inspired by an extensive literature on Total Quality Management. After about a decade with these frameworks, we can see that punctuality has risen by about 2–3 percentage points. However, this pace of improvements is slower than desired. We propose that there is a gap between what most individual improvement efforts deliver, and what can be detected by directly monitoring punctuality. This gap stifles the desired culture of constant improvements. We instead propose a methodology for how to monitor punctuality improvements, by focusing on the constituents of a train trip. Using 20 years of data from commuter trains in three metropolitan regions (Stockholm, Gothenburg & Malmö), we show the frequency of runtime and dwell time delays is directly related to punctuality. These delay frequencies are also easy to measure and target, and more easily capture the intended effects of specific improvement efforts. Our hope is that this framework and measures such as these will better enable systematic efforts to improve railway punctuality.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Robustness and Adaptability of Reinforcement Learning-Based Cooperative
           Autonomous Driving in Mixed-Autonomy Traffic

    • Authors: Rodolfo Valiente;Behrad Toghi;Ramtin Pedarsani;Yaser P. Fallah;
      Pages: 397 - 410
      Abstract: Building autonomous vehicles (AVs) is a complex problem, but enabling them to operate in the real world where they will be surrounded by human-driven vehicles (HVs) is extremely challenging. Prior works have shown the possibilities of creating inter-agent cooperation between a group of AVs that follow a social utility. Such altruistic AVs can form alliances and affect the behavior of HVs to achieve socially desirable outcomes. We identify two major challenges in the co-existence of AVs and HVs. First, social preferences and individual traits of a given human driver, e.g., selflessness and aggressiveness are unknown to an AV, and it is almost impossible to infer them in real-time during a short AV-HV interaction. Second, contrary to AVs that are expected to follow a policy, HVs do not necessarily follow a stationary policy and therefore are extremely hard to predict. To alleviate the above-mentioned challenges, we formulate the mixed-autonomy problem as a multi-agent reinforcement learning (MARL) problem and propose a decentralized framework and reward function for training cooperative AVs. Our approach enables AVs to learn the decision-making of HVs implicitly from experience, optimizes for a social utility while prioritizing safety and allowing adaptability; robustifying altruistic AVs to different human behaviors and constraining them to a safe action space. Finally, we investigate the robustness, safety and sensitivity of AVs to various HVs behavioral traits and present the settings in which the AVs can learn cooperative policies that are adaptable to different situations.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • A Method of Developing Quantile Convolutional Neural Networks for Electric
           Vehicle Battery Temperature Prediction Trained on Cross-Domain Data

    • Authors: Andreas M. Billert;Michael Frey;Frank Gauterin;
      Pages: 411 - 425
      Abstract: The energy consumption caused by battery thermal management of electric vehicles can be reduced using predictive control. A predictive controller needs a prediction model of the battery temperature, for example for different battery cooling and heating thresholds. In the proposed method, cross-domain data from simulation, vehicle fleet and weather stations were analyzed and processed as training data for a Convolutional Neural Network (CNN). The CNN took data from previous road segments and predictions for following road segments as input and predicted the change in battery temperature as quantile sequences over a prediction horizon. Properties of the collected cross-domain data sets were analyzed and considered during preprocessing, before 150 models were trained, of which the best performing model was further analyzed. Point-forecast metrics and quantile-related metrics were used for model comparison and evaluation. For example, the median prediction achieved a mean absolute error (MAE) of 0.27 °C and the true values were below the median prediction in 47% of the test data. Possible improvements of the method such as increasing data size, using more complex architectures as well as optimizing the horizon sizes were discussed. In conclusion, the method was able to well predict battery temperatures for different battery cooling thresholds.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Automotive Lidar and Vibration: Resonance, Inertial Measurement Unit, and
           Effects on the Point Cloud

    • Authors: Birgit Schlager;Thomas Goelles;Marco Behmer;Stefan Muckenhuber;Johann Payer;Daniel Watzenig;
      Pages: 426 - 434
      Abstract: Lidar is an important component of the perception suite for automated systems. The effects of vibration on lidar point clouds are mostly unknown, despite the lidar’s wide adaption and usual application under conditions where vibration occurs frequently. In this study, we performed controlled vibration tests from 6 to 2000 Hz at 9 and 12 m/s2 in vertical direction on the automotive lidar OS1-64 by Ouster. An information loss emerged which is mostly independent from frequency and acceleration. The loss of points is randomly distributed and does not correlate with range, intensity, or ring number (the horizontal line of the rotating lidar unit). The resonance frequency of 1426 Hz proved to be unproblematic as no pronounced negative effects on the point cloud could be identified. For vibration detection, the internal Inertial Measurement Unit (IMU) of the OS1-64 is accurate and sufficient for vibrations up to 50 Hz. Above 50 Hz, external IMUs would be required for vibration detection. Counting the number of points on a target close to the edges was investigated as an exemplary way to detect vibration purely based on the point cloud, i.e., independent of the lidar’s IMU.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Efficiency of Connected Semi-Autonomous Platooning Bus Services in
           High-Demand Transit Corridors

    • Authors: Wei Zhang;Erik Jenelius;Hugo Badia;
      Pages: 435 - 448
      Abstract: The paper investigates the efficiency of serving high demand transit corridors with connected semi-autonomous bus platoons in both bus and BRT services. Platooning facilitates higher capacity than conventional buses by forming virtual long buses out of multiple smaller vehicles, which may be particularly relevant in scenarios with large demand variations between peak and off-peak hours. The problem is formulated as a constrained optimization problem to minimize total system cost, which includes waiting cost, access cost, riding cost, operating cost and capital cost. For a single period with fixed demand, both analytical solutions and numerical examples are provided. Sensitivity analysis is carried out with regard to demand levels and capacity upper bound. The problem is generalized to a two-period problem considering peak and off-peak demand. Numerical results are provided with sensitivity analysis regarding demand level and ratio of peak/off-peak demand. Furthermore, the impact of a lower bound on service headway is investigated. The result shows that semi-autonomous vehicle platooning is competitive in medium and high-demand scenarios, with the potential of reduced user costs and operating costs at the expense of additional rolling stock costs. Minimum headway constraint, restricted vehicle size, and higher demand ratio all make semi-autonomous platooning more advantageous.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Safe Driving Model Based on V2V Vehicle Communication

    • Authors: Hua Xie;Yunjia Wang;Xieyang Su;Shengchun Wang;Liang Wang;
      Pages: 449 - 457
      Abstract: Along with the rapid development of connected vehicle communication technology, describing the vehicle following driving status becomes gradually complicated. Driver behavior, vehicle type, and road factors affect vehicle speed, and the following distance reflects variability. In this paper, a nonlinear following distance model is constructed to characterize this variability. The model is based on the full speed difference model (FVD), and introduces the headway time distance coefficient, the following vehicle type coefficient, the communication advance response parameter reflecting the driver’s personal characteristics, and the slope coefficient and curve curvature coefficient reflecting the road conditions, etc., and analyzes to obtain the stability conditions of the model. MATLAB is applied to numerical simulation experiments of the model, and the results show that the model can better describe the variability of following headway due to driver attributes, vehicle type, slope and curve in the connected vehicle scenario, thus providing a reference for traffic flow control and management.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing

    • Authors: Johannes Betz;Hongrui Zheng;Alexander Liniger;Ugo Rosolia;Phillip Karle;Madhur Behl;Venkat Krovi;Rahul Mangharam;
      Pages: 458 - 488
      Abstract: The rising popularity of self-driving cars has led to the emergence of a new research field in recent years: Autonomous racing. Researchers are developing software and hardware for high-performance race vehicles which aim to operate autonomously on the edge of the vehicle’s limits: High speeds, high accelerations, low reaction times, highly uncertain, dynamic, and adversarial environments. This paper represents the first holistic survey that covers the research in the field of autonomous racing. We focus on the field of autonomous racecars only and display the algorithms, methods, and approaches used in the areas of perception, planning, control, and end-to-end learning. Further, with an increasing number of autonomous racing competitions, researchers now have access to high-performance platforms to test and evaluate their autonomy algorithms. This survey presents a comprehensive overview of the current autonomous racing platforms, emphasizing the software-hardware co-evolution to the current stage. Finally, based on additional discussion with leading researchers in the field, we conclude with a summary of open research challenges that will guide future researchers in this field.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Analysis of Route Choice During Planned and Unplanned Road Closures

    • Authors: Jairaj Desai;Benjamin Scholer;Jijo K. Mathew;Howell Li;Darcy M. Bullock;
      Pages: 489 - 502
      Abstract: The Federal Highway Administration (FHWA) Alternate Route Handbook proposes guidance to identify alternate routes during planned and unplanned road closures. A challenge with this process is the lack of traffic data available to decision-makers. High volume corridors experiencing unplanned closures can provide a rich case history by systematically collecting connected vehicle (CV) data during such incidents. CV data provide the ability to directly measure actual diversion routes and travel times during an ongoing or historical incident. This paper presents methodologies to systematically analyze diversion data to identify the most common alternate route choices and impacted interstate exits, valuable information for public safety and transportation agencies to evaluate the surrounding road network’s resiliency in accommodating diverting traffic. Agencies can use this information to proactively deploy resources (officers, signs, barricades) at critical locations during future closures. The scalability of this methodology is demonstrated by evaluating 12 additional cases to assess diversion rates found to be in the range of 58% to 93% for total closures exceeding five hours. The paper concludes by recommending agencies apply these methodologies to develop data-driven diversion strategies on critical routes coupled with real-time CV monitoring in dispatch centers to provide agile adjustment of resources along diversion routes.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Physics-Informed Deep Learning for Traffic State Estimation: Illustrations
           With LWR and CTM Models

    • Authors: Archie J. Huang;Shaurya Agarwal;
      Pages: 503 - 518
      Abstract: We present a physics-informed deep learning (PIDL) approach to tackle the challenge of data sparsity and sensor noise in traffic state estimation (TSE). PIDL strengthens a deep learning (DL) neural network with the knowledge of traffic flow theory to accurately estimate traffic conditions. The ‘physics’—a priori information of the system—acts as a regularization agent during training. We illustrate the implementation of the proposed approach with two commonly used models representing traffic physics: Lighthill-Whitham-Richards (LWR) model and the cell transmission model (CTM). The LWR implementation is illustrated with Greenshields’ and inverse-lambda fundamental diagrams; whereas, CTM model implementation works with any fundamental diagram of choice. Two case studies validate the approach by reconstructing the velocity-field. Case study-I uses synthetic data generated to resemble the trajectory of connected and autonomous vehicles as captured by roadside units. Case study-II employs NGSIM data mimicking scant probe vehicle observations. We observe that the proposed PIDL approach is particularly better in state estimation with a lower amount of training data, illustrating the capability of PIDL in making precise and timely TSE even with sparse input. E.g., With 10% CAV penetration rate and a 15% added-noise, relative error for PIDL was at 22.9% compared to 30.8% for DL.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
  • Using Ontologies for the Formalization and Recognition of Criticality for
           Automated Driving

    • Authors: Lukas Westhofen;Christian Neurohr;Martin Butz;Maike Scholtes;Michael Schuldes;
      Pages: 519 - 538
      Abstract: Knowledge representation and reasoning has a long history of examining how knowledge can be formalized, interpreted, and semantically analyzed by machines. In the area of automated vehicles, recent advances suggest the ability to formalize and leverage relevant knowledge as a key enabler in handling the inherently open and complex context of the traffic world. This paper demonstrates ontologies to be a powerful tool for a) modeling and formalization of and b) reasoning about factors associated with criticality in the environment of automated vehicles. For this, we leverage the well-known 6-Layer Model to create a formal representation of the environmental context. Within this representation, an ontology models domain knowledge as logical axioms, enabling deduction on the presence of critical factors within traffic scenarios. For executing automated analyses, a joint description logic and rule reasoner is used in combination with an a-priori predicate augmentation. We elaborate on the modular approach, present a publicly available implementation, and exemplarily evaluate the method by means of a large-scale drone data set of urban traffic scenarios.
      PubDate: 2022
      Issue No: Vol. 3 (2022)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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