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
International Journal of Intelligent Transportation Systems Research
Journal Prestige (SJR): 0.301
Citation Impact (citeScore): 1
Number of Followers: 13  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1868-8659 - ISSN (Online) 1868-8659
Published by Springer-Verlag Homepage  [2469 journals]
  • Intelligent Traffic Light via Policy-based Deep Reinforcement Learning

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      Abstract: Abstract Intelligent traffic lights in smart cities can optimally reduce traffic congestion. In this study, we employ reinforcement learning to train the control agent of a traffic light on a simulator of urban mobility. As a difference from existing works, a policy-based deep reinforcement learning method, Proximal Policy Optimization (PPO), is utilized rather than value-based methods such as Deep Q Network (DQN) and Double DQN (DDQN). First, the obtained optimal policy from PPO is compared to those from DQN and DDQN. It is found that the policy from PPO performs better than the others. Next, instead of fixed-interval traffic light phases, we adopt light phases with variable time intervals, which result in a better policy to pass the traffic flow. Then, the effects of environment and action disturbances are studied to demonstrate that the learning-based controller is robust. Finally, we consider unbalanced traffic flows and find that an intelligent traffic light can perform moderately well for the unbalanced traffic scenarios, although it learns the optimal policy from the balanced traffic scenarios only.
      PubDate: 2022-08-12
       
  • Multi-Vehicle Tracking Using Heterogeneous Neural Networks for Appearance
           And Motion Features

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      Abstract: Abstract This paper presents a multi-vehicle tracking algorithm using appearance feature and motion history based on heterogeneous deep learning aimed at autonomous driving applications. Our proposed multi-vehicle tracking model follows the tracking-by-detection paradigm. To track multiple vehicles, we utilize the appearance and motion features of the target vehicles in consecutive frames. The proposed multi-vehicle tracking system employs a deep convolutional neural network, which is trained with a triplet loss minimization method to extract appearance features. The key contribution of the proposed method lies in a Long Short-Term Memory (LSTM) with a fully connected layer that accurately predicts the probability distribution of the next appearance and motion features of tracked objects. We constructed a multi-vehicle tracking dataset from various real road traffic using a camera sensor on a vehicle. To evaluate our proposed algorithm, we use several multi-target tracking datasets from the KITTI object tracking benchmark, which is a Public tracking dataset, as well as our evaluation dataset. Experimental results demonstrate that the proposed multi-vehicle tracking algorithm achieves a MOTA of 84.5% and MOTP 86.3% on the KITTI tracking dataset, and a MOTA of 81.8% and MOTP 84.8% on our evaluation dataset, an improvement of 8.6% and 9.6% over the previous methods.
      PubDate: 2022-08-10
       
  • Self-tuning Look-ahead Distance of Pure-pursuit Path-following Control for
           Autonomous Vehicles Using an Automated Curve Information Extraction Method
           

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      Abstract: Abstract Autonomous driving vehicles have recently gained a lot of attention and are still a work in progress. The goal of this research is to improve the behavior of the autonomous vehicle for path tracking problems in which the steering performance degrades when travelling on curved paths. The pure pursuit controller is a good choice for solving this problem because of its simplicity, accurately, and speed of computation, but tuning its parameters to produce customized proper behavior requires a significant amount of effort. The autonomous computation of a curve identification procedure to manage the change in road curvature was offered as a novel idea for self-tuning of a look-ahead distance. I proposed a technique that splits the path into lines and circular-arcs portions first, and then extracts curve information that would be utilized to calculate the proper look-ahead distances later. Furthermore, the PI controller is used in conjunction with the pure pursuit controller to reduce the existing cross track error. To demonstrate the efficiency of the suggested method, the entire algorithms were implemented in the MATLAB/ Simulink environment for various path shapes. The results show that the novel strategy may greatly minimize lateral errors on curving roadways while maintaining acceptable tracking accuracy.
      PubDate: 2022-08-02
       
  • Sharp Curve Detection of Autonomous Vehicles using DBSCAN and Augmented
           Sliding Window Techniques

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      Abstract: Abstract Deviation of the car from the lane is very dangerous and it leads to crashes. Hence, lane detection is one of the most important features that helps in maintaining the car stay in the respective lane. The main goal of this paper is to detect the lanes with road lane markings with higher accuracy under sharp curve scenarios. In this paper, lane points are extracted using image processing techniques, lane detection of solid lines and dashed lines using a combination of Augmented sliding windows and Clustering technique is discussed. Lanes are simulated in a laboratory for an input data set of 1388 images. The input data set consists of curved lines of dashed and solid lines which split and merge. This combined technique (Augmented sliding windows + clustering) is mainly used to detect the split lanes scenario. Further partially obscured lanes are also tested with the algorithm. The center of the lanes throughout the lane is calculated. Missing lane markings of obscured lanes are also estimated using the relative position of adjacent lanes based on lane width. The detection accuracy of Clustering and Augmented sliding window for the considered input dataset of 1388 frames is 97.70%. The algorithm is showing reliable accuracy in detecting the sharp curves.
      PubDate: 2022-08-02
       
  • A Novel Adaptive Traffic Signal Control Based on Cloud/Fog/Edge Computing

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      Abstract: Abstract This paper proposes the Internet of Things-based real-time adaptive traffic signal control strategy. The proposed model consists of three-layer; edge computing layer, fog computing layer, and cloud computing layer. The edge computing layer provides real-time and local optimization. The middle layer, which is the fog computing layer, performs a real-time and global optimization process. The cloud computing layer, which is the top layer, acts as a control center and optimizes the parameters of the fog layer and the edge layer. The proposed strategy uses the Deep Q-Learning algorithm for the optimization process in all three layers. This study employs the SUMO traffic simulator for performance evaluation. These results are compared with the results of adaptive traffic control methods. The output of this study shows that the proposed model can reduce waiting times and travel times while increasing travel speed.
      PubDate: 2022-08-01
       
  • Capacity Analysis of Urban Arterial Midblock Sections Under Mixed Traffic
           Conditions

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      Abstract: Abstract The main aim of the study is to model speed and to determine the capacity of six-lane divided urban arterials by developing speed models. The various midblock sections are selected such that there is no influence of side frictions like pedestrian, on-street parking, etc. Speed-flow data was gathered at six midblock sections of urban arterial roads and extracted for every 5-min interval. The speed–flow relationship as observed from field data replicates the parabolic relationship between two parameters, and hence it may be hypothesized that the speed–density data would follow a linear relationship. Traffic density has been measured by using the basic relationships among speed, flow, and density. This linear speed-density relationship for heterogeneous traffic situations has been used, and efforts are put on to develop simultaneous speed equations for various vehicle categories. These equations are solved by developing a MATLAB software program, and variation in speed with traffic composition and traffic volume on the road has been explained. Simultaneous equations are used to develop the speed-flow plot and capacity of the midblock sections as 5892 PCU/hr for one direction of flow. The study outcomes are useful to estimate the capacity of midblock sections using volume as input data. Once the speed is dropped to a congestion level, the section reaches a congested state, and traffic operations become chaotic. The congestion mitigation measures may be employed by rerouting existing traffic to reduce the congestion on the urban roadway network.
      PubDate: 2022-08-01
       
  • Estimation Method of Parking Space Conditions Using Multiple 3D-LiDARs

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      Abstract: Abstract In the early stages of the spread of autonomous vehicles, it is conceivable to operate an automated valet parking system in parking lots where autonomous vehicles and non-autonomous vehicles coexist. Since non-autonomous vehicles may park beyond the parking space, it is necessary to estimate parking space conditions three-dimensionally. This paper proposes a method to estimate the parking space conditions using multiple 3D-LiDARs that can detect the space three-dimensionally. In the evaluation experiment, multiple 3D-LiDARs were installed in the parking lot of a public facility, and the estimation accuracy of the proposed method was evaluated in various situations.
      PubDate: 2022-08-01
       
  • Modeling Analysis of Automated and Connected Cars in Signalized
           Intersections

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      Abstract: Abstract It is acknowledged that many of the problems related to urban congestion can be solved through the diffusion of automated vehicles capable not only of replacing drivers, but also of receiving information from the infrastructure. In this article, the effects of driverless cars (level 3–4 of automation) and of the Green Light Optimal Speed Advisory (GLOSA) system, a particular kind of Cooperative – Intelligent Transport System (C-ITS), will be evaluated at an urban signalized intersection through a set of micro-simulations. The aim of the paper is to analyze the two system as stand-alone before evaluating their jointed implementation, so to obtain their impacts and to analyze if and how they synergize for different levels of market penetration. The results of these simulations demonstrate that automated and connected cars should bring global benefits at intersections and also result in a first set of recommendations and best practices for the implementation of the systems in the short-medium term. Particular focus is given to the interaction between the equipped vehicles and traditional traffic, to frame the negative effects on the overall crossing both in Traffic Efficiency and Environment. Finally, the evaluation of a real crossing in Milan is performed and the results of the overall node are provided for different scenarios and time horizon.
      PubDate: 2022-08-01
       
  • Stochastic Optimisation for the Design of Energy-Efficient Controllers for
           Cooperative Truck Platoons

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      Abstract: Abstract Cooperative adaptive cruise control systems have the potential to improve fuel efficiency and safety. However, due to the large amount of uncertainties, which are encountered in platooning applications, typical controller calibrations are often not reliable. Therefore, in order to ensure a satisfying performance in the presence of various information topologies and relevant uncertainties such as errors in data transmission or extreme manoeuvres of the lead vehicle, a risk-averse stochastic optimisation approach for controller calibration is suggested and demonstrated for a pre-existing control scheme. Realistic vehicle dynamics simulation experiments with a prescribed set of uncertainties, such as transmission delays and different vehicle parameters, are performed. The results show that the collision probability and energy consumption are reduced by the risk-averse calibration of the controller and its spacing policy compared with classical calibration which assumes perfect communication.
      PubDate: 2022-08-01
       
  • A GPS-based Algorithm for Brake and Turn Detection

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      Abstract: Abstract Driving behavior recognition is a notable topic in travel safety, as transportation and insurance companies could adopt effective tools to detect unsafe driving and internalize the associated costs. Different driving events and the related severity must be detected to distinguish abnormal behaviors. The global positioning system (GPS) provides useful information regarding the location of the vehicle at any time and is vastly used in various devices such as smartphones and GPS trackers. Other sensors, on the other hand, provide complementary valuable information but their implementation requires extra costs and more complex and intensive algorithms. We developed a threshold-based algorithm to detect the turning and braking of vehicles using the GPS sensor. The data contained 11 trips with a frequency of 1 Hz with a total duration of 2.7 h. The algorithm utilizes a supplementary map matching and a relabeling technique to boost the accuracy and yet preserve the reasonable computation load. The overall precision and recall rate of the turn-detecting model are respectively 77.5% and 92.5%. Also, this algorithm can detect braking events with a precision of 68.18% and a recall of 83.33%. To address the concerns about the overfitting, we tested our algorithm on a secondary dataset, and nearly similar values of accuracy were resulted, showing the flexible nature of our algorithm while dealing with a different set of driving behaviors and road characteristics. Additionally, a sensitivity analysis showed the sensitive nature of the brake detection algorithm, in contrast with the turn detection algorithm. Overall, our algorithm showed promising results and can be a pioneer one in the field of low-cost detection algorithms built for smartphones or GPS trackers possessed by various trucking and car insurance companies.
      PubDate: 2022-08-01
       
  • Measuring Road Roughness through Crowdsourcing while Minimizing the
           Conditional Effects

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      Abstract: Abstract A well-maintained road network is a crucial factor for sustainable urban development. Over the past few years, researchers have proposed smartphone-based crowdsourced applications as a low-cost effective solution to acquire frequent road surface quality updates. One of the main limitations faced by these applications is that the collected values exhibit significant variations over the conditions under which the road data was collected. This study is an attempt to develop a road roughness monitoring platform using passenger cars that can produce accurate results while reducing the effect of these conditions such as the car type, smartphone model, or its placement. The developed system consists of several features including automatic journey detection, freedom to use any smartphone in any position with or without an active internet connection when collecting data, converging values collected from different sources, and visualizing them in a virtual map. A set of field tests were carried out to evaluate the proposed system based on the road condition, passenger car type, smartphone model, and smartphone placement inside the vehicle. The results show that the proposed solution is effective in predicting accurate values after reducing the effect of these varying factors.
      PubDate: 2022-06-28
       
  • The Car-Following Model Based on the Drivers' Psychological
           Characteristics

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      Abstract: Abstract In this paper, the car-following model based on drivers' psychological characteristics is proposed for developing drivers' explicit behavior decision under complicated driving environment. The nature of driving behavior in different driving mode is discussed based on the concept of decision-making system, using the drivers' psychological individuality combined with universality of vehicle kinetics. Especially, the perceived distance and expected safety distance are considered rather than the traditional space headway. The genetic algorithm and NGSIM (New Generation Simulation) data are used to calibrate parameters in car-following model. It is shown in the calibration results that the drivers have different concerns in different driving mode, and it is logical for the drivers' psychological characteristics. The effectiveness and accuracy of proposed model have been verified compared to the GM and FVD model in simulation results.
      PubDate: 2022-06-15
       
  • Highway Lane-Changing Prediction Using a Hierarchical Software
           Architecture based on Support Vector Machine and Continuous Hidden Markov
           Model

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      Abstract: Abstract Lane changing behavior is one of the most essential and complex driving attributes. The lack of proper lane changing behavior can lead to collisions and traffic congestion. In this work, a novel hierarchical software architecture for the prediction of lane changing behavior on highways has been developed and evaluated. The two-layer hierarchical structure of the proposed model is based on a support vector machine (SVM) in the first layer followed by another model based on continuous Hidden Markov Model (HMM) incorporated with a Gaussian Mixture Model (GMM) in the second layer. The trajectory classification predicted in the first layer by the SVM is binary, i.e., Lane Change (LC) and Lane Keep (LK) behaviors. The second layer of the software architecture further classifies the LC behavior output of the first layer to left-lane change (LLC) and right-lane change (RLC) behaviors using the model of continuous HMM (CHMM) incorporated with GMM. The developed model has been evaluated using the real-world dataset of U.S. Highway 101 and Interstate 80 from Federal Highway Administration’s Next Generation Simulation (NGSIM). The first layer prediction is performed within an approximately 10 seconds time window. The positions, velocity and Time to Collision (TTC) of the target and surrounding vehicles are taken as input parameters in the model execution of the second layer. The test results show that the proposed hierarchical model exhibits 91% accuracy for LLC, 87% accuracy for RLC and 99% accuracy for LK behaviors. This model can be effectively used as a lane changing suggestion system in the advanced driver assistance systems (ADAS).
      PubDate: 2022-06-02
      DOI: 10.1007/s13177-022-00308-2
       
  • Proposed Policy to Manage the Barrier of the Implementation of Intelligent
           Transportation System

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      Abstract: Abstract This study has two objectives. First, this study investigated the barriers and their relationship that make ITS project development in Semarang City still hard. Second, this study aimed to propose some policies to mitigate those barriers. This study identifies 16 ITS project implementation barriers based on the previous research. Then, to fulfill the research objectives, this study employs three distinct methods: content validity analysis as a technique to measuring how well the factors correspond to or reflect a barrier to the ITS project’s implementation; interpretive structural modeling (ISM) as a technique for determining the direct or indirect between barriers; and Delphi as the technique to find the consensus of proposed policies to mitigate the significant barriers. The result of data processing with ISM indicated that internal organizational barriers in the context of the timing of procedure for writing plans, divided responsibilities, and the right organizational system to drive ITS project’s implementation occupied the topmost level. This barrier is affected by the lower level and has less influence than the remaining barriers. The result of data processing with the ISM method also indicated three very significant barriers to the ITS project’s implementation, namely low interoperability of the system at the Department of Transportation Semarang City, which is making it challenging to integrate the ITS-based transportation system; lack of involvement of related institutions to build long-term commitment and awareness that ITS project’s implementation has potential lack of awareness that the ITS project’s implementation has the potential to reduce crashes and save lives; and political problems (political short-termism or discontinuities due to political cycles). Then, the result of data processing with Delphi proposed and validated several policies to mitigate those barriers.
      PubDate: 2022-06-02
      DOI: 10.1007/s13177-022-00310-8
       
  • A Data-Driven Approach for Traffic Crash Prediction: A Case Study in
           Ningbo, China

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      Abstract: Abstract In the past few years, fully connected Long Short-Term Memory (FC-LSTM) network has been widely used to predict traffic crashes in urban areas. This article attempts to improve the traditional prediction model by adopting Convolutional Long Short-Term Memory (ConvLSTM) network. ConvLSTM can effectively capture the spatial and temporal characteristics of traffic crashes within road network. It overcomes the shortcoming of the FC-LSTM model that ignores the spatial characteristics of traffic crashes. Therefore, the ConvLSTM model shows excellent performance when predicting traffic crashes. To verify the effectiveness of the ConvLSTM, this study uses historical crash data in the City of Ningbo to train the model and compares the result with that from FC-LSTM. The results show that ConvLSTM has better accuracy and lower loss values. Moreover, the model has higher calculation efficiency. Therefore, the ConvLSTM model is more suitable for predicting traffic crashes.
      PubDate: 2022-06-01
      DOI: 10.1007/s13177-022-00307-3
       
  • Automated Truck Taxonomy Classification Using Deep Convolutional Neural
           Networks

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      Abstract: Abstract Trucks are the key transporters of freight. The types of commodities and goods mainly determine the right trailer for carrying them. Furthermore, finding the commodities’ flow is an important task for transportation agencies in better planning freight infrastructure investments and initiating near-term traffic throughput improvements. In this paper, we propose a fine-grained deep learning based truck classification system that can detect and classify the trucks, tractors, and trailers following the Federal Highway Administration’s (FHWA) vehicle schema. We created a large, fine-grained labeled dataset of vehicle images collected from state highways. Experimental results show the high accuracy of our system and visualize the salient features of the trucks that influence classification.
      PubDate: 2022-05-11
      DOI: 10.1007/s13177-022-00306-4
       
  • Dynamic Weight-based Multi-Objective Reward Architecture for Adaptive
           Traffic Signal Control System

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      Abstract: Abstract An Adaptive Traffic Signal Control (ATSC) system uses real-time traffic information to control traffic lights and makes the public transport system more reliable and accessible. Deep Reinforcement Learning (DRL) has recently demonstrated its use in resolving traffic signal control problems. However, designing a good reward function is one of the most crucial aspects of DRL since the system learns to make proper decisions based on reward. Furthermore, the multi-objective reward function is preferable for the ATSC system, which is more challenging than designing a single objective. The existing multi-objective reward functions use pre-defined fixed weights to combine the multiple parameters, which requires rigorous training and cannot represent the actual impact of the parameters. To solve this problem, we proposed a new reward architecture called Dynamic Weights Multi-objective Reward Architecture (DWMORA) for ATSC. It calculates the weights instantly based on the current traffic condition to ensure the actual impact of the parameters. A comparative result study of the proposed approach with several existing reward functions shows the improvement of the road traffic in terms of waiting time, travel time, and halting number.
      PubDate: 2022-04-29
      DOI: 10.1007/s13177-022-00305-5
       
  • Autonomous Bus Pilot Project Testing and Demonstration using Light Rail
           Transit Track

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      Abstract: Abstract The rapidly aging society of Japan requires a quality transportation system that provides new mobility services combining innovative technologies to include all residents. The present study carries out a pilot test of the world’s first connected public transport system between autonomous buses (AB) and the light rail transit (LRT). In this system, telecommunication between two modes enabled the bus to fully self-drive and pull in precisely at an LRT stop. Reporting a result of the pilot test in Hiroshima, this paper analyzes (a) the feasibility of the system based on a monitor survey and (b) public acceptance based on a resident survey of local residents.
      PubDate: 2022-04-21
      DOI: 10.1007/s13177-021-00264-3
       
  • Cellular Automata Model for Lane Changing Activity

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      Abstract: Abstract In the present work, a microscopic traffic flow model using Cellular Automata is proposed. The model is intended to explain accurately the lane changing activity, which is treated as a continuous process rather than a discrete event as suggested by previous models. Various important properties of traffic flow could be explained by the proposed model and the simulated results are quite reasonable. The proposed model can give explanation to the microscopic properties, like, local stability, asymptotic stability, closing-in/shying-away and insensitivity of safe distance headway to perturbation pattern, initial distance headway and initial speed. Macroscopic properties like speed, flow and density were studied for a traffic stream under various conditions like varying road width. The results obtained from simulation of single lane and wide roads with lane discipline match closely to the values suggested by Highway Capacity Manual.
      PubDate: 2022-04-18
      DOI: 10.1007/s13177-022-00302-8
       
  • Effect of Acceleration and Deceleration Information of Preceding Vehicle
           Group on Fuel Economy of Following Vehicle in Starting

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      Abstract: Abstract This study looked at the effectiveness of acceleration/deceleration information regarding preceding and pre-preceding vehicles on the driving behavior and fuel economy of the following vehicle. As a result, it was suggested that information provision may improve the fuel economy of the following vehicle. It was also found that the subjects that increased fuel economy tended to value information on the acceleration/deceleration of the pre-preceding vehicle compared to those that decreased fuel economy did. From the above, it was indicated that the provision of information on the acceleration/deceleration of a preceding vehicle group to a following vehicle was effective.
      PubDate: 2022-04-02
      DOI: 10.1007/s13177-022-00303-7
       
 
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