Subjects -> TRANSPORTATION (Total: 216 journals)
    - AIR TRANSPORT (9 journals)
    - AUTOMOBILES (26 journals)
    - RAILROADS (10 journals)
    - ROADS AND TRAFFIC (9 journals)
    - SHIPS AND SHIPPING (39 journals)
    - TRANSPORTATION (123 journals)

TRANSPORTATION (123 journals)                     

Showing 1 - 53 of 53 Journals sorted alphabetically
Accident Analysis & Prevention     Hybrid Journal   (Followers: 123)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 9)
Applied Mobilities     Hybrid Journal   (Followers: 3)
Archives of Transport     Open Access   (Followers: 18)
Asian Transport Studies     Open Access   (Followers: 1)
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Case Studies on Transport Policy     Hybrid Journal   (Followers: 16)
Cities in the 21st Century     Open Access   (Followers: 17)
Danish Journal of Transportation Research / Dansk Tidsskrift for Transportforskning     Open Access   (Followers: 3)
Decision Making : Applications in Management and Engineering     Open Access   (Followers: 2)
Economics of Transportation     Partially Free   (Followers: 14)
Emission Control Science and Technology     Hybrid Journal   (Followers: 2)
eTransportation     Open Access   (Followers: 2)
EURO Journal of Transportation and Logistics     Hybrid Journal   (Followers: 15)
European Transport Research Review     Open Access   (Followers: 24)
Geosystem Engineering     Hybrid Journal   (Followers: 2)
IATSS Research     Open Access  
IEEE Open Journal of Intelligent Transportation Systems     Open Access   (Followers: 7)
IEEE Vehicular Technology Magazine     Full-text available via subscription   (Followers: 7)
IET Electrical Systems in Transportation     Open Access   (Followers: 11)
IET Intelligent Transport Systems     Open Access   (Followers: 12)
IET Smart Cities     Open Access  
IFAC-PapersOnLine     Open Access   (Followers: 1)
International Journal of Applied Logistics     Full-text available via subscription   (Followers: 11)
International Journal of Crashworthiness     Hybrid Journal   (Followers: 12)
International Journal of e-Navigation and Maritime Economy     Open Access   (Followers: 6)
International Journal of Electric and Hybrid Vehicles     Hybrid Journal   (Followers: 11)
International Journal of Electronic Transport     Hybrid Journal   (Followers: 9)
International Journal of Heavy Vehicle Systems     Hybrid Journal   (Followers: 7)
International Journal of Intelligent Transportation Systems Research     Hybrid Journal   (Followers: 16)
International Journal of Mobile Communications     Hybrid Journal   (Followers: 9)
International Journal of Ocean Systems Management     Hybrid Journal   (Followers: 3)
International Journal of Physical Distribution & Logistics Management     Hybrid Journal   (Followers: 14)
International Journal of Services Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Sustainable Transportation     Hybrid Journal   (Followers: 19)
International Journal of Traffic and Transportation Engineering     Open Access   (Followers: 19)
International Journal of Transportation Engineering     Open Access   (Followers: 2)
International Journal of Transportation Science and Technology     Open Access   (Followers: 12)
International Journal of Vehicle Systems Modelling and Testing     Hybrid Journal   (Followers: 3)
Journal of Advanced Transportation     Hybrid Journal   (Followers: 16)
Journal of Big Data Analytics in Transportation     Hybrid Journal   (Followers: 2)
Journal of Intelligent and Connected Vehicles     Open Access   (Followers: 2)
Journal of KONES     Open Access  
Journal of Mechatronics, Electrical Power, and Vehicular Technology     Open Access   (Followers: 6)
Journal of Modern Transportation     Full-text available via subscription   (Followers: 9)
Journal of Navigation     Hybrid Journal   (Followers: 286)
Journal of Sport & Social Issues     Hybrid Journal   (Followers: 12)
Journal of Sustainable Mobility     Full-text available via subscription   (Followers: 3)
Journal of Traffic and Transportation Engineering (English Edition)     Open Access   (Followers: 5)
Journal of Transport & Health     Hybrid Journal   (Followers: 12)
Journal of Transport and Land Use     Open Access   (Followers: 27)
Journal of Transport and Supply Chain Management     Open Access   (Followers: 16)
Journal of Transport Geography     Hybrid Journal   (Followers: 28)
Journal of Transport History     Hybrid Journal   (Followers: 13)
Journal of Transportation Safety & Security     Hybrid Journal   (Followers: 10)
Journal of Transportation Security     Hybrid Journal   (Followers: 2)
Journal of Transportation Systems Engineering and Information Technology     Full-text available via subscription   (Followers: 12)
Journal of Transportation Technologies     Open Access   (Followers: 15)
Journal of Waterway Port Coastal and Ocean Engineering     Full-text available via subscription   (Followers: 8)
Journal on Vehicle Routing Algorithms     Hybrid Journal  
Les Dossiers du Grihl     Open Access   (Followers: 1)
LOGI ? Scientific Journal on Transport and Logistics     Open Access   (Followers: 1)
Logistics     Open Access   (Followers: 3)
Logistics & Sustainable Transport     Open Access   (Followers: 6)
Logistique & Management     Hybrid Journal  
Mobility in History     Full-text available via subscription   (Followers: 5)
Modern Transportation     Open Access   (Followers: 12)
Nonlinear Dynamics     Hybrid Journal   (Followers: 20)
Open Journal of Safety Science and Technology     Open Access   (Followers: 18)
Open Transportation Journal     Open Access   (Followers: 1)
Packaging, Transport, Storage & Security of Radioactive Material     Hybrid Journal   (Followers: 4)
Periodica Polytechnica Transportation Engineering     Open Access  
Pervasive and Mobile Computing     Hybrid Journal   (Followers: 8)
Proceedings of the Institution of Mechanical Engineers Part F: Journal of Rail and Rapid Transit     Hybrid Journal   (Followers: 15)
Promet : Traffic &Transportation     Open Access  
Public Transport     Hybrid Journal   (Followers: 21)
Recherche Transports Sécurité     Hybrid Journal   (Followers: 1)
Research in Transportation Business and Management     Partially Free   (Followers: 8)
Revista Transporte y Territorio     Open Access   (Followers: 1)
Revue Marocaine de Management, Logistique et Transport     Open Access  
Romanian Journal of Transport Infrastructure     Open Access   (Followers: 1)
SourceOCDE Transports     Full-text available via subscription   (Followers: 2)
Sport, Education and Society     Hybrid Journal   (Followers: 13)
Sport, Ethics and Philosophy     Hybrid Journal   (Followers: 3)
Streetnotes     Open Access   (Followers: 1)
Synthesis Lectures on Mobile and Pervasive Computing     Full-text available via subscription   (Followers: 1)
Tire Science and Technology     Full-text available via subscription   (Followers: 3)
Transactions on Transport Sciences     Open Access   (Followers: 7)
Transport     Open Access   (Followers: 17)
Transport and Telecommunication     Open Access   (Followers: 5)
Transport in Porous Media     Hybrid Journal   (Followers: 2)
Transport Problems     Open Access   (Followers: 5)
Transport Reviews: A Transnational Transdisciplinary Journal     Hybrid Journal   (Followers: 10)
Transport technic and technology     Open Access   (Followers: 1)
Transportation     Hybrid Journal   (Followers: 35)
Transportation Engineering     Open Access   (Followers: 1)
Transportation Geotechnics     Full-text available via subscription   (Followers: 1)
Transportation in Developing Economies     Hybrid Journal  
Transportation Infrastructure Geotechnology     Hybrid Journal   (Followers: 8)
Transportation Journal     Full-text available via subscription   (Followers: 17)
Transportation Letters : The International Journal of Transportation Research     Hybrid Journal   (Followers: 6)
Transportation Research Interdisciplinary Perspectives     Open Access   (Followers: 3)
Transportation Research Part A: Policy and Practice     Hybrid Journal   (Followers: 41)
Transportation Research Part B: Methodological     Hybrid Journal   (Followers: 39)
Transportation Research Part C: Emerging Technologies     Hybrid Journal   (Followers: 31)
Transportation Research Procedia     Open Access   (Followers: 7)
Transportation Research Record : Journal of the Transportation Research Board     Full-text available via subscription   (Followers: 36)
Transportation Safety and Environment     Open Access   (Followers: 2)
Transportation Science     Full-text available via subscription   (Followers: 26)
Transportation Systems and Technology     Open Access  
TRANSPORTES     Open Access   (Followers: 6)
Transportmetrica A : Transport Science     Hybrid Journal   (Followers: 9)
Transportmetrica B : Transport Dynamics     Hybrid Journal   (Followers: 1)
Transportrecht     Hybrid Journal   (Followers: 1)
Travel Behaviour and Society     Full-text available via subscription   (Followers: 12)
Travel Medicine and Infectious Disease     Hybrid Journal   (Followers: 4)
Urban Development Issues     Open Access   (Followers: 3)
Urban, Planning and Transport Research     Open Access   (Followers: 33)
Vehicles     Open Access  
Vehicular Communications     Full-text available via subscription   (Followers: 4)
World Electric Vehicle Journal     Open Access   (Followers: 1)
World Review of Intermodal Transportation Research     Hybrid Journal   (Followers: 6)
Транспортні системи та технології перевезень     Open Access  

           

Similar Journals
Journal Cover
Journal of Advanced Transportation
Journal Prestige (SJR): 0.581
Citation Impact (citeScore): 1
Number of Followers: 16  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 0197-6729 - ISSN (Online) 2042-3195
Published by Hindawi Homepage  [344 journals]
  • Moving Camera-Based Object Tracking Using Adaptive Ground Plane Estimation
           and Constrained Multiple Kernels

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      Abstract: Moving camera-based object tracking method for the intelligent transportation system (ITS) has drawn increasing attention. The unpredictability of driving environments and noise from the camera calibration, however, make conventional ground plane estimation unreliable and adversely affecting the tracking result. In this paper, we propose an object tracking system using an adaptive ground plane estimation algorithm, facilitated with constrained multiple kernel (CMK) tracking and Kalman filtering, to continuously update the location of moving objects. The proposed algorithm takes advantage of the structure from motion (SfM) to estimate the pose of moving camera, and then the estimated camera’s yaw angle is used as a feedback to improve the accuracy of the ground plane estimation. To further robustly and efficiently tracking objects under occlusion, the constrained multiple kernel tracking technique is adopted in the proposed system to track moving objects in 3D space (depth). The proposed system is evaluated on several challenging datasets, and the experimental results show the favorable performance, which not only can efficiently track on-road objects in a dashcam equipped on a free-moving vehicle but also can well handle occlusion in the tracking.
      PubDate: Wed, 21 Jul 2021 07:05:01 +000
       
  • Stochastic Programming of Sustainable Waste Cooking Oil for Biodiesel
           Supply Chain under Uncertainty

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      Abstract: As an important emission reduction source for the transportation industry, biofuel has received strong support from the Chinese government. However, the development of the biofuel industry is still struggling. The high degree of uncertainty makes the development of the industry face huge challenges. Kitchen waste, as a biodiesel raw material with a large yield, has good development prospects. Reuse of kitchen waste can solve public health and safety problems. This paper proposes a two-stage stochastic programming model under supply disturbance to optimize the supply chain from the perspective of contract. Then current three main flow directions of kitchen waste are analysed and the reasonable price for biodiesel operators to purchase is determined. By signing contracts with the biodiesel operators, restaurant is guaranteed and encouraged to provide a certain percentage of kitchen waste to meet the demand for biodiesel production. Using actual case in the Yangtze River Delta region, the performance of the stochastic programming model under disturbance was compared. Through the sensitivity analysis of different parameters, this paper determines the influence of its supply chain network design and expected total system cost. Through the optimization of the waste cooking oil (WCO) for biodiesel supply chain, this paper can effectively improve the efficiency of the supply chain, reduce system costs, increase the profits of biofuel operators, and promote the sustainable development of the biofuel industry.
      PubDate: Tue, 20 Jul 2021 12:35:01 +000
       
  • Exploring the Citywide Human Mobility Patterns of Taxi Trips through a
           Topic-Modeling Analysis

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      Abstract: Understanding human mobility patterns is of great importance for a wide range of applications from social networks to transportation planning. Toward this end, the spatial-temporal information of a large-scale dataset of taxi trips was collected via GPS, from March 10 to 23, 2014, in Beijing. The data contain trips generated by a great portion of taxi vehicles citywide. We revealed that the geographic displacement of those trips follows the power law distribution and the corresponding travel time follows a mixture of the exponential and power law distribution. To identify human mobility patterns, a topic model with the latent Dirichlet allocation (LDA) algorithm was proposed to infer the sixty-five key topics. By measuring the variation of trip displacement over time, we find that the travel distance in the morning rush hour is much shorter than that in the other time. As for daily patterns, it shows that taxi mobility presents weekly regularity both on weekdays and on weekends. Among different days in the same week, mobility patterns on Tuesday and Wednesday are quite similar. By quantifying the trip distance along time, we find that Topic 44 exhibits dominant patterns, which means distance less than 10 km is predominant no matter what time in a day. The findings could be references for travelers to arrange trips and policymakers to formulate sound traffic management policies.
      PubDate: Tue, 20 Jul 2021 09:20:00 +000
       
  • Correlation Analysis of External Environment Risk Factors for High-Speed
           Railway Derailment Based on Unstructured Data

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      Abstract: In railway operation, unsafe events such as faults may occur, and a large number of unsafe event records are generated in the process of unsafe events’ recording and reporting. Unsafe events have been described in unstructured natural language, which often has inconsistent structure and complex sources, involving multiple railway specialties, with multisource, heterogeneous, and unstructured characteristics. In practical application, the efficiency of processing is extremely low, leading to potentially unsafe management utilization. Based on the data on unsafe events, this paper utilizes big data processing technology, conducts association rules mining and association degree analysis, extracts the word segmentation, and obtains the feature vector of unsafe fault event data. At the same time, the unsafe event data analysis model is constructed in combination with regular expression and pattern matching technology. This paper establishes the matching model of high-speed railway derailment-based external environment risk factors and applies it to the occurrence of unsafe events. This model could be utilized to analyze and excavate the link between external environment risk factors and the occurrence of unsafe events and carry out the automatic extraction of characteristic information such as risk possibility and consequence severity; hence, it has potential for identifying, with enhanced accuracy, high-risk factors that may lead to high-speed railway derailment. Based on this study, we could make full use of the unsafe event data, forecast the risk trend, and discover the law of high-speed railway derailment. This study introduces a viable approach to analyzing the unsafe event data, forecasting risk trend, and conceptualizing high-speed railway derailment. It could also enforce the accurate quantification of high-speed railway safety situation, refine the risk index and conduct in-depth analysis combined with the model, and effectively support the digitalization and intellectualization of high-speed railway operation safety.
      PubDate: Tue, 20 Jul 2021 08:50:01 +000
       
  • Effect of Cognitive Distraction on Physiological Measures and Driving
           Performance in Traditional and Mixed Traffic Environments

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      Abstract: Distracted driving is a dominant cause of traffic accidents. In addition, with the rapid development of intelligent vehicles, mixed traffic environments are expected to become more complicated with multiple types of intelligent vehicles sharing the road, thereby increasing the opportunities for distracted driving. However, the existing research on detecting driver distraction in mixed traffic environments is limited. Therefore, in this study, we analysed the effect of cognitive distraction on the driver physiological measures and driving performance in traditional and mixed traffic environments and compared the parameters extracted in the two environments. Sixty drivers were involved in the data collection, which included normal driving and two distracting tasks while driving in a simulator. Repeated-measures analysis of variance (ANOVA) was performed to examine the effect of cognitive distraction and traffic environments on all parameters. The results indicate that the effects of the pupil diameter, standard deviations (SDs) of the horizontal and vertical fixation angles, blink frequency, speed, SD of the lane positioning (SDLP), SD of the steering wheel angle (SDSWA), and steering entropy (SE) were significant. These findings provide a theoretical foundation for identifying the most appropriate parameters to detect cognitive distraction in traditional and mixed traffic environments to help reduce traffic accidents.
      PubDate: Mon, 19 Jul 2021 11:05:01 +000
       
  • An Online Map Matching Algorithm Based on Second-Order Hidden Markov Model

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      Abstract: Map matching is a key preprocess of trajectory data which recently have become a major data source for various transport applications and location-based services. In this paper, an online map matching algorithm based on the second-order hidden Markov model (HMM) is proposed for processing trajectory data in complex urban road networks such as parallel road segments and various road intersections. Several factors such as driver’s travel preference, network topology, road level, and vehicle heading are well considered. An extended Viterbi algorithm and a self-adaptive sliding window mechanism are adopted to solve the map matching problem efficiently. To demonstrate the effectiveness of the proposed algorithm, a case study is carried out using a massive taxi trajectory dataset in Nanjing, China. Case study results show that the accuracy of the proposed algorithm outperforms the baseline algorithm built on the first-order HMM in various testing experiments.
      PubDate: Mon, 19 Jul 2021 10:05:00 +000
       
  • Research on Road Adhesion Condition Identification Based on an Improved
           ALexNet Model

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      Abstract: Automotive intelligence has become a revolutionary trend in automotive technology. Complex road driving conditions directly affect driving safety and comfort. Therefore, by improving the recognition accuracy of road type or road adhesion coefficient, the ability of vehicles to perceive the surrounding environment will be enhanced. This will further contribute to vehicle intelligence. In this paper, considering that the process of manually extracting image features is complicated and that the extraction method is random for everyone, road surface condition identification method based on an improved ALexNet model, namely, the road surface recognition model (RSRM), is proposed. First, the ALexNet network model is pretrained on the ImageNet dataset offline. Second, the weights of the shallow network structure after training, including the convolutional layer, are saved and migrated to the proposed model. In addition, the fully connected layer fixed to the shallow network is replaced by 2 to 3, which improves the training accuracy and shortens the training time. Finally, the traditional machine learning and improved ALexNet model are compared, focusing on adaptability, prediction output, and error performance, among others. The results show that the accuracy of the proposed model is better than that of the traditional machine learning method by 10% and the ALexNet model by 3%, and it is 0.3 h faster than ALexNet in training speed. It is verified that RSRM effectively improves the network training speed and accuracy of road image recognition.
      PubDate: Sat, 17 Jul 2021 07:50:00 +000
       
  • Multilane Microscopic Modeling to Measure Mobility and Safety Consequences
           of Mixed Traffic in Freeway Weaving Sections

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      Abstract: Weaving sections are components of highway networks that introduce a heightened likelihood for bottlenecks and collisions. Automated vehicle technology could address this as it holds considerable promise for transportation mobility and safety improvements. However, the implications of combining automated vehicles (AuVs) with traditional human-driven vehicles (HuVs) in weaving freeway sections have not been quantitatively measured. To address this gap, this paper objectively experimented with bidirectional (i.e., longitudinal and lateral) motion dynamics in a microscopic modeling framework to measure the mobility and safety implications for mixed traffic movement in a freeway weaving section. Our research begins by establishing a multilane microscopic model for studied vehicle types (i.e., AuV and HuV) from model predictive control with the provision to form a CACC platoon of AuV vehicles. The proposed modeling framework was tested first with HuV only on a two-lane weaving section and validated using standardized macroscopic parameters from the Highway Capacity Manual. This model was then applied to incrementally expand the AuV share for varying inflow rates of traffic. Simulation results showed that the maximum flow rate through the weaving section was attained at a 65% AuV share. At the same time, steadiness in the average speed of traffic was experienced with increasing AuV share. The results also revealed that a 95% AuV share could reduce potential conflicts by 94.28%. Finally, the results of simulated scenarios were consolidated and scaled to report expected mobility and safety outcomes from the prevailing traffic state and the optimal AuV share for the current inflow rate in weaving sections.
      PubDate: Thu, 15 Jul 2021 10:05:00 +000
       
  • A Deep Pedestrian Tracking SSD-Based Model in the Sudden Emergency or
           Violent Environment

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      Abstract: Public security monitoring is a hot issue that the government and citizens pay close attention to. Multiobject tracking plays an important role in solving many problems for public security. Under crowded scenarios and emergency places, it is a challenging problem to predict and warn owing to the complexity of crowd intersection. There are still many deficiencies in the research of multiobject trajectory prediction, which mostly employ object detection and data association. Compared with the tremendous progress in object detection, data association still relied on hand-crafted constraints such as group, motion, and spatial proximity. Emergencies usually have the characteristics of mutation, target diversification, low illumination, or resolution, which makes multitarget tracking more difficult. In this paper, we harness the advance of the deep learning framework for data association in object tracking by jointly modeling pedestrian features. The proposed deep pedestrian tracking SSD-based model can pair and link pedestrian features in any two frames. The model was trained with open dataset, and the results, accuracy, and speed of the model were compared between normal and emergency or violent environment. The experimental results show that the tracking accuracy of mAP is higher than 95% both in normal and abnormal data sets and higher than that of the traditional detection algorithm. The detection speed of the normal data set is slightly higher than that of the abnormal data set. In general, the model has good tracking results and credibility for multitarget tracking in emergency environment. The research provides technical support for safety assurance and behavior monitoring in emergency environment.
      PubDate: Thu, 15 Jul 2021 08:05:01 +000
       
  • Robustness Analysis of Air Route Network Based on Topology Potential and
           Relative Entropy Methods

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      Abstract: Air route network (ARN) is the important carrier of air transport, and its robustness has important influence on the safety and stability of air transport. To analyze the robustness of ARN, in this paper, a topology potential relative entropy (TPRE) model is proposed, based on topology potential (TP) and relative entropy (RE) methods. Firstly, the TPRE model is established as the theoretical basis for the research. Secondly, an air route reduction network (ARRN) model is constructed according to real Chinese ARN. Besides, the basic topology features of ARRN are given by complex network theory. To prove the applicability, objectivity, and accuracy of the proposed method, attack strategies including random, degree, betweenness, closeness, eigenvector, and Bonacich are used to attack ARRN. Eventually, the performance of ARRN robustness is analyzed by network efficiency, size of giant component, and the proposed TPRE model. This conclusion has practical significance for optimizing ARN structure and improving airspace efficiency.
      PubDate: Wed, 14 Jul 2021 10:05:00 +000
       
  • Track Behavior and Crash Risk Analysis of Passenger Cars on Hairpin Curves
           of Two-Lane Mountain Roads

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      Abstract: Hairpin curves are often employed in alignment layout and an important feature that identifies dangerous driving conditions for mountain roads. However, driving behaviors at hairpin curves remain ambiguous. Field driving tests were conducted in this study on one two-lane mountain road with 11 hairpin curves. Vehicle-mounted equipment was utilized to collect track and lateral distance between the wheels and the lane markings under naturally driving conditions. Track morphology and patterns, risks, and road crash mechanisms were analyzed. The main findings are as follows. Curve cutting was a typical method for negotiating hairpin curves, was observed for left and right turns, and can be classified into three types based on the location of the cutting point, namely, cutting at curve entry, cutting at curve middle, and cutting at curve exit. Based on the lateral positional relationships between tracks and lane markings, six track patterns are determined for left turns and four track patterns for right turns. When passing a right turn by cutting the curve, a driver occupied the right shoulder of the turn; therefore, there is a risk of colliding with the mountain or the guardrail. When making a left turn into hairpin curves, a driver occupied the right shoulder on curve exit, resulting in running off the road or colliding with the guardrail. More than 70% and 60% of drivers occupied the opposite lane when turning right and turning left, respectively, into a hairpin turn, which led to intertwining between the tracks in the two driving directions and therefore a risk of potential collisions.
      PubDate: Tue, 13 Jul 2021 12:50:01 +000
       
  • A Novel Adaptive Visual Analytics Framework for Multiship Encounter
           Identification

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      Abstract: The automatic identification of multiship encounter is a vital criterion for ship collision avoidance and intelligent maritime safety surveillance. However, the parameters of ship encounter identification in the existing studies are fixed, and the methods are weak to give an automatic and visual performance in the multiship encounter identification. In order to fix the existed gap, this paper proposed a novel adaptive visual analytics framework for automatic multiship encounter identification based on density-based spatial clustering of applications with noise (DBSCAN) and visual analytics by adjusting the parameters of ship encounter adaptively. The DBSCAN clustering method was applied to detect the clusters of encounter ships and filter out the nonencounter ship, and the distribution and density of the encounter ship had been visualized on the nautical chart to give a better perception of ships’ behavior with a potentially high navigational risk. The framework had been designed and developed using DBSCAN and visual analytics, and the effectiveness was evaluated and validated by adjusting different parameters of multiship encounter within the Southwest waters of Zhoushan Island, China. The results showed that the proposed framework had a good performance in the visual identification of multiship encounter within confined waters, which could assist the ship collision avoidance and intelligent maritime surveillance system.
      PubDate: Tue, 13 Jul 2021 09:20:01 +000
       
  • SAV Operations on a Bus Line Corridor: Travel Demand, Service Frequency,
           and Vehicle Size

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      Abstract: Before shared automated vehicles (SAVs) can be widely adopted, they are anticipated to be implemented commercially in confined regions or fixed routes where the benefits of automation can be realized. SAVs have the potential to operate in a traditional transit corridor, replacing conventional transit vehicles, and have frequent interactions with riders and other vehicles sharing the same right of way. This paper microsimulates SAVs’ operation on a 6.5-mile corridor to understand how vehicle size and attributes of such SAV-based transit affect traffic, transit riders, and system costs. The SUMO (Simulation of Urban MObility) platform is employed to model microscopic interactions among SAVs, transit passengers, and other traffic. Results show that the use of smaller, but more frequent, SAVs leads to reduced passenger waiting times but increased vehicle travel times. More frequent services of smaller SAVs do not, in general, significantly affect general traffic due to shorter dwell times. Overall, using smaller SAVs instead of the large 40-seat SAVs can reduce system costs by up to 4% while also reducing passenger waiting times, under various demand levels and passenger loading factors. However, the use of 5-seat SAVs does not always have the lowest system costs.
      PubDate: Tue, 13 Jul 2021 08:50:01 +000
       
  • Highway Travel Time Prediction of Segments Based on ANPR Data considering
           Traffic Diversion

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      Abstract: Travel time is one of the most critical parameters in proactive traffic management and the deployment of advanced traveler information systems. This paper proposes a hybrid model named LSTM-CNN for predicting the travel time of highways by integrating the long short-term memory (LSTM) and the convolutional neural networks (CNNs) with the attention mechanism and the residual network. The highway is divided into multiple segments by considering the traffic diversion and the relative location of automatic number plate recognition (ANPR). There are four steps in this hybrid approach. First, the average travel time of each segment in each interval is calculated from ANPR and fed into LSTM in the form of a multidimensional array. Second, the attention mechanism is adopted to combine the hidden layer of LSTM with dynamic temporal weights. Third, the residual network is introduced to increase the network depth and overcome the vanishing gradient problem, which consists of three pairs of one-dimensional convolutional layers (Conv1D) and batch normalization (BatchNorm) with the rectified linear unit (ReLU) as the activation function. Finally, a series of Conv1D layers is connected to extract features further and reduce dimensionality. The proposed LSTM-CNN approach is tested on the three-month ANPR data of a real-world 39.25 km highway with four pairs of ANPR detectors of the uplink and downlink, Zhejiang, China. The experimental results indicate that LSTM-CNN learns spatial, temporal, and depth information better than the state-of-the-art traffic forecasting models, so LSTM-CNN can predict more accurate travel time. Moreover, LSTM-CNN outperforms the state-of-the-art methods in nonrecurrent prediction, multistep-ahead prediction, and long-term prediction. LSTM-CNN is a promising model with scalability and portability for highway traffic prediction and can be further extended to improve the performance of the advanced traffic management system (ATMS) and advanced traffic information system (ATIS).
      PubDate: Mon, 12 Jul 2021 10:35:01 +000
       
  • Deep Learning-Based Dynamic Stable Cluster Head Selection in VANET

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      Abstract: VANET is the spontaneous evolving creation of a wireless network, and clustering in these networks is a challenging task due to rapidly changing topology and frequent disconnection in networks. The cluster head (CH) stability plays a prominent role in robustness and scalability in the network. The stable CH ensures minimum intra- and intercluster communication, thereby reducing the overhead. These challenges lead the authors to search for a CH selection method based on a weighted amalgamation of four metrics: befit factor, community neighborhood, eccentricity, and trust. The stability of CH depends on the vehicle’s speed, distance, velocity, and change in acceleration. These all are included in the befit factor. Also, the accurate location of the vehicle in changing the model is very vital. Thus, the predicted location with the Kalman filter’s help is used to evaluate CH stability. The results have shown better performance than the existing state of the art for the befit factor. The change in dynamics and frequent disconnection in communication links due to the vehicle’s high speed are inevitable. To comprehend this problem, a graphing approach is used to evaluate the eccentricity and the community neighborhood. The link reliability is calculated using the eigengap heuristic. The last metric is trust; this is one of the concepts that has not been included in the weighted approach to date as per the literature. An adaptive spectrum sensing is designed for evaluating the trust values specifically for the primary users. A deep recurrent learning network, commonly known as long short-term memory (LSTM), is trained for the probability of detection with various signals and noise conditions. The false rate has drastically reduced with the usage of LSTM. The proposed scheme is tested on the real map of Chengdu, southwestern China’s Sichuan province, with different vehicular mobilities. The comparative study with the individual and weighted metric has shown significant improvement in the cluster head stability during high vehicular density. Also, there is a considerable increase in network performance in energy, packet delay, packet delay ratio, and throughput.
      PubDate: Mon, 12 Jul 2021 10:05:01 +000
       
  • Route Selection of Multimodal Transport Based on China Railway
           Transportation

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      Abstract: The advantage of multimodal transport is that it can deliver the goods to their destination in a reasonable combination of transport modes while ensuring security and punctuality. Multimodal transportation can effectively reduce logistics costs, improve logistics efficiency, and reduce environmental pollution. In the process of multimodal transportation, due to the interference of natural factors (weather, terrain, etc.) and some special human factors, it may have different degrees of impact on the transportation time and transportation safety of different transportation modes. Therefore, when choosing a transportation method, it is necessary to consider the transportation time and transportation safety under the interference. However, the current research on multimodal transport has not considered the impact of external interference on transportation time and transportation safety. Compared with other modes of transportation, external interference has a relatively small impact on railway transportation. Railways can safely deliver goods to their destinations on time. Under the background of China’s huge railway network and advanced heavy-duty technology, this paper establishes a multimodal transport route selection model for considering railway as the core, introduces time penalty cost and damage compensation cost, and takes the lowest comprehensive transportation cost as the model objective under the premise of considering transportation reliability and transportation safety. Finally, taking a multimodal transport network in China as an example, an improved ant colony algorithm is used to solve the model and the results verify the rationality of the model.
      PubDate: Mon, 12 Jul 2021 07:35:00 +000
       
  • Effect of Adaptive Cruise Control on Mixed Traffic Flow: A Comparison of
           Constant Time Gap Policy with Variable Time Gap Policy

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      Abstract: With the emerging application of low-level driving automation technology, heterogeneous traffic flow mixed with human-driven vehicles and low-level autonomous vehicles is dawning. In this context, it is imperative to investigate its effect on mixed traffic flow. As a key component for adaptive cruise control (ACC) which is a practical low-level application of driving automation, the time gap policy determines the dynamic of ACC-equipped vehicles and plays a crucial role in traffic flow stability and efficiency. There are two main time gap policies used for ACC at present, namely, constant time gap (CTG) policy and variable time gap (VTG) policy. In this study, we carried out a detailed comparison between these time gap policies to investigate their potential effect on mixed traffic flow, where the analytical- and simulation-based approaches are both considered. Analytical results show that VTG policy is superior to CTG policy in stabilizing the mixed traffic flow. In addition, numerical simulations are also conducted and simulation results further support the analytical results. As for throughput, there is no difference between CTG policy and VTG policy in analytical progress when the same time gap is set at the equilibrium. However, simulation results based on an on-ramp scenario show that the throughput of mixed traffic flow with VTG policy is slightly higher than that of CTG policy. Meanwhile, the scatter of mixed traffic flow with VTG policy in the flow-density diagram gradually clusters in the middle range of density (i.e., 20–40 veh/km) with the increase of the penetration rates of ACC vehicles, where the traffic flow operates more efficiently. These results indicate that VTG policy is better than CTG policy when designing controllers for ACC in the context of traffic flow operation and control.
      PubDate: Sat, 10 Jul 2021 08:20:00 +000
       
  • Friend-Invitation Promotion Scheme Used in Electric Carsharing: Empirical
           Analysis and Policy Implications

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      Abstract: The combination of electric vehicle (EV) and carsharing is expected to provide social and environmental benefits, like encouraging sustainable travel behaviors (reducing car ownership and vehicle kilometres of travel) and improving the accessibility and flexibility of urban transport. Thus, electric carsharing is encouraged to be adopted for daily trips, and the operators propose the friend-invitation promotion scheme for the membership expansion. This study explores the effectiveness of this scheme and the characteristics of the scheme participants and their invited friends (e.g., age, friend-invitation pattern, and EV rental pattern). The analysis found that 28.4% of these invited friends would make at least one EV rental after registration, whereas 30.4% of the other members who registered in the same period would do so, indicating that these invited friends were less active. Therefore, suggestions are given based on the EV rental pattern of these invited friends (preferring a longer journey using a smaller but cheaper EV) to enhance the effectiveness of the friend-invitation promotion scheme.
      PubDate: Fri, 09 Jul 2021 11:05:01 +000
       
  • Evaluating the Safety Impact of Connected and Autonomous Vehicles with
           Lane Management on Freeway Crash Hotspots Using the Surrogate Safety
           Assessment Model

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      Abstract: One goal for large-scale deployment of connected and autonomous vehicles is to achieve the traffic safety benefit since connected and autonomous vehicles (CAVs) could reduce the collision risk by enhancing the driver’s situation perception ability. Previous studies have analyzed the safety impact of CAVs involved in traffic, but only few studies examined the safety benefits brought by CAVs when approaching high-collision-risk road segments such as the freeway crash hotspots. This study chooses one freeway crash hotspot in Wuhan, China, as an instance and attempts to estimate the safety benefits for differential penetration rates (PRs) of CAVs using the surrogate safety assessment model (SSAM). First, the freeway crash hotspot is identified with kernel density estimation and simulated by VISSIM. Then, the intelligent driver model (IDM) and Wiedemann 99 (a car-following model) are adopted and calibrated to control the driving behaviors of CAVs and human-driven vehicles (HVs) in this study, respectively. The impact that rather CAVs are constrained with or without managed lanes on traffic safety is also discussed, and the PR of CAVs is set from 10% to 90%. The results of this study show that when the PR of CAVs is lower than 50%, there is no significant improvement on the safety measures such as conflicts, acceleration, and velocity difference, which are extracted from the vehicle trajectory data using SSAM. When the penetration rate is over 70%, the experiment results demonstrate that the traffic flow passing the freeway hotspot is with fewer conflicts, smaller acceleration, and smaller velocity difference in the scenario where CAVs are constrained with managed lane compared with the scenario without managed lane control. The safety benefit that CAVs bring needs to be discussed. The lane management of CAVs will also lead to distinct safety impact.
      PubDate: Wed, 07 Jul 2021 07:05:01 +000
       
  • The Effects of Multitasking and Tools Carried by Travelers Onboard on the
           Perceived Trip Time

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      Abstract: Travelers conduct onboard activities while using the tools they bring with them onboard to convert part of their travel time to a productive time. Productive travel time contributes to the reduction in the disutility of travel time. This paper discusses the influence of travelers’ onboard activities and the tools carried by travelers on the perceived trip time. 10 onboard activities and 12 tools carried by travelers are introduced and studied in this work. A questionnaire focusing on the main trip of each respondent in urban areas is conducted, where a sample size of 525 participants is collected. Statistical methods such as central tendency, chi-square, exploratory factor analysis (EFA), rank-based nonparametric test, and multivariate analysis of variance (MANOVA) are applied. The main findings are the following: almost all of the onboard activities and the tools carried by travelers impact the trip time positively (i.e., the perception is enhanced). For each transport mode, the most frequent onboard activities that impact the trip time positively is obtained, and the connection between each onboard activity and each tool carried by travelers is found (i.e., moderate to strong association). EFA uncovers the underlying relationship between those onboard activities and those tools carried by travelers that influence travelers’ perception. In this case, instead of the full list, fewer onboard activities and tools carried by travelers are produced to simplify the finding of their impacts on the perceived trip time. The participation in onboard activity is ranked across certain groups, such as the tendency of women to be engaged in onboard activities is higher than men’s tendency. Regarding the positive impact on trip time, a statistical difference is demonstrated between groups, where the use of the tools carried by travelers is varied across the transport mode, trip purpose, and trip time, gender, age, education, and job variable. Besides, the involvement in onboard activities is statistically dependent across the transport mode, gender, income, and car ownership variable. The output of this study helps decision-makers and mobility planners in understanding the behavior of travelers onboard in more detail, such as the availability of onboard tools affecting the choice of transport mode.
      PubDate: Tue, 06 Jul 2021 09:05:01 +000
       
  • Two-Sided Matching on Comprehensive Transportation Network Emergency
           Vehicles’ Allocation

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      Abstract: In emergency rescue, the allocation of comprehensive transportation network emergency vehicles often affects the efficiency of the whole rescue process. In the context of disasters, this paper researches the one-to-many two-sided matching problem between the emergency vehicles and the materials to be transported. Firstly, based on the needs of both parties involved in the matching, the satisfaction evaluation systems are constructed; with the goal of maximizing the weighted satisfaction of the affected areas and vehicles, the optimization model of the materials and emergency vehicles matching is established; then, an improved National Intern Matching Program (NIMP) algorithm is designed to solve the model, which is based on the k: 1 experimental pairing and updating ideas, and can take into account the capacity and destination constraints of vehicles in the matching process. Finally, through the calculation of an example, the matching scheme can make the satisfaction of material transportation reach 0.7392, and the simulation analysis proves that the scheme keeps certain stability in risky conditions.
      PubDate: Mon, 05 Jul 2021 11:05:02 +000
       
  • Short-Term Traffic Flow Prediction: A Method of Combined Deep Learnings

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      Abstract: Short-term traffic flow prediction can provide a basis for traffic management and support for travelers to make decisions. Accurate short-term traffic flow prediction also provides necessary conditions for the sustainable development of the traffic environment. Although the application of deep learning methods for traffic flow prediction has achieved good accuracy, the problem of combining multiple deep learning methods to improve the prediction accuracy of a single method still has a margin for in-depth research. In this article, a combined deep learning prediction (CDLP) model including two paralleled single deep learning models, CNN-LSTM-attention model and CNN-GRU-attention model, is established. In the model, a one-dimensional convolutional neural network (1DCNN) is used to extract traffic flow local trend features and RNN variants (LSTM and GRU) with attention mechanism are used to extract long temporal dependencies trend features. Moreover, a dynamic optimal weighted coefficient algorithm (DOWCA) is proposed to calculate the dynamic weights of CNN-LSTM-attention and CNN-GRU-attention with the goal of minimizing the sum of squared errors of the CDLP model. Then, the neuron number, loss function, optimization algorithm, and other parameters of the CDLP model are discussed and set through experiments. Finally, the training set and test set for the CDLP model are established through the processing of traffic flow data collected from the field. The CDLP model is trained and tested, and the prediction results of traffic flow are obtained and analyzed. It indicates that the CDLP model can fit the change trend of traffic flow very well and has better performance. Furthermore, under the same dataset, the results from the CDLP model are compared with baseline models. It is found that the CDLP model has higher prediction accuracy than baseline models.
      PubDate: Mon, 05 Jul 2021 08:05:01 +000
       
  • Autonomous Bus Fleet Control Using Multiagent Reinforcement Learning

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      Abstract: Autonomous buses are becoming increasingly popular and have been widely developed in many countries. However, autonomous buses must learn to navigate the city efficiently to be integrated into public transport systems. Efficient operation of these buses can be achieved by intelligent agents through reinforcement learning. In this study, we investigate the autonomous bus fleet control problem, which appears noisy to the agents owing to random arrivals and incomplete observation of the environment. We propose a multi-agent reinforcement learning method combined with an advanced policy gradient algorithm for this large-scale dynamic optimization problem. An agent-based simulation platform was developed to model the dynamic system of a fixed stop/station loop route, autonomous bus fleet, and passengers. This platform was also applied to assess the performance of the proposed algorithm. The experimental results indicate that the developed algorithm outperforms other reinforcement learning methods in the multi-agent domain. The simulation results also reveal the effectiveness of our proposed algorithm in outperforming the existing scheduled bus system in terms of the bus fleet size and passenger wait times for bus routes with comparatively lesser number of passengers.
      PubDate: Sat, 03 Jul 2021 06:50:01 +000
       
  • Two-Stage Humanitarian Logistics Deprivation Model for the Planning of
           Scarce KN-95 Facemask Supplies under Agent’s Cooperation

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      Abstract: Humanitarian logistics encompasses a wide spectrum of conditions or constraints for supply chains, yet its focus on mitigating human suffering efficiently is what has motivated organizations and governments to make rapid decisions in real time. In this article, through the approach to an emergency such as COVID-19, we propose a two-stage model capable of considering human suffering, the cost of humanitarian logistics, and the benefit obtained by the interaction of suppliers that generally behave as oligopolies through a mathematical programming model and one of the cooperative games. Our main finding was the adaptability of a previously validated model for humanitarian logistics to the ongoing COVID-19 pandemic, where the externalities had greater relevance in social costs than private costs.
      PubDate: Fri, 02 Jul 2021 09:50:00 +000
       
  • Optimal Tradable Credit Scheme Design with Recommended Credit Price

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      Abstract: As an interesting research topic in transportation field, tradable credit scheme (TCS) has been extensively explored in the latest decade. Existing studies implicitly assumed that travelers are clear about the equilibrium credit price and make their trips accordingly. However, this may not be the case in reality, since the credit price is endogenously determined by the credit-trading behavior, especially in the early stages after the implementation of a TCS. Considering travelers’ uncertainty on the equilibrium credit price, this paper aims to investigate the impacts of perception error on credit price and how to accommodate such errors by an appropriate scheme design. Transferring the perception error on credit price to a given and fixed value released by central authority, we first investigate the impacts of recommended credit price under a given TCS. The numerical results imply that it is necessary to simultaneously consider the choice of recommended credit price and charging scheme in TCS design. Regarding this, we combine the goals of social welfare and public acceptance of the scheme and propose a bilevel biobjective programming (BLBOP) model, by which the net economic benefit is maximized while the gap between the recommended and realized credit prices is minimized. Through two numerical examples, it is found that the rise in perception variance could intensify the contradiction effect between the two objectives. Additionally, a nonnegligible price gap must be allowed to occur to maintain the effectiveness of a TCS.
      PubDate: Fri, 02 Jul 2021 08:05:01 +000
       
  • Assessing Accessibility of Dockless Sharing-Bike Networks by the Social
           Network Analysis Method

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      Abstract: Dockless sharing bikes play an increasingly significant role in transit transfer, especially for the first/last mile. However, it is not always accessible for users to find sharing bicycles. The objective of this paper is to assess the accessibility of dockless sharing bikes from a network perspective, which would provide a decision-making basis not only for potential bike users but also for urban planners, policymakers, and bicycle suppliers to optimize sharing-bike systems. Considering bicycle travel characteristics, a hierarchical clustering algorithm was applied to construct the dockless sharing-bike network. The social network analysis (SNA) method was adopted to assess the accessibility of the bike network. Then, a spatial interaction model was chosen to conduct a correlation analysis to compare the accessibility obtained from the SNA approach. The case study of Shanghai indicates a strong connection between the accessibility and the SNA indicators with the correlation coefficient of 0.779, which demonstrates the feasibility of the proposed method. This paper contributes to a deep understanding of dockless sharing-bike network accessibility since the SNA approach considers both the interaction barriers and the network structure of a bicycle network. The developed methodology requires fewer data and is easy to operate. Thus, it can serve as a tool to facilitate the smart management of sharing bikes for improving a sustainable transportation system.
      PubDate: Thu, 01 Jul 2021 10:20:01 +000
       
  • 2.5D Facial Personality Prediction Based on Deep Learning

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      Abstract: The assessment of personality traits is now a key part of many important social activities, such as job hunting, accident prevention in transportation, disease treatment, policing, and interpersonal interactions. In a previous study, we predicted personality based on positive images of college students. Although this method achieved a high accuracy, the reliance on positive images alone results in the loss of much personality-related information. Our new findings show that using real-life 2.5D static facial contour images, it is possible to make statistically significant predictions about a wider range of personality traits for both men and women. We address the objective of comprehensive understanding of a person’s personality traits by developing a multiperspective 2.5D hybrid personality-computing model to evaluate the potential correlation between static facial contour images and personality characteristics. Our experimental results show that the deep neural network trained by large labeled datasets can reliably predict people’s multidimensional personality characteristics through 2.5D static facial contour images, and the prediction accuracy is better than the previous method using 2D images.
      PubDate: Wed, 30 Jun 2021 09:35:00 +000
       
  • Deviation of Pedestrian Path due to the Presence of Building Entrances

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      Abstract: Commercial areas, especially urban ones with numerous buildings, are becoming increasingly prone to congestion because of their popularity. Visual inspections show that interactions between pedestrians and building entrances affect the distribution of pedestrian trajectories, which influences the utility of pedestrian spaces and the design of urban shopping areas. Herein, we analyse the dynamics of pedestrian deviations around building entrances. We used a video recorded using an unmanned aerial vehicle to determine pedestrian trajectories in a Chinese commercial walking space. First, the candidate variables affecting deviation behaviours were determined via correlation testing. Second, two regression models were developed by considering the deviation behaviours of pedestrians walking past a building entrance. The models suggest that the starting position of a pedestrian’s deviation, the total pedestrian flow at the building entrance, the density in an area in the vicinity of the entrance, and the number of interacting pedestrians impact the total distance traversed during path deviation.
      PubDate: Wed, 30 Jun 2021 07:50:01 +000
       
  • How Do Different Treatments of Catchment Area Affect the Station Level
           Demand Modeling of Urban Rail Transit'

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      Abstract: Direct demand modeling is a useful tool to estimate the demand of urban rail transit stations and to determine factors that significantly influence such demand. The construction of a direct demand model involves determination of the catchment area. Although there have been many methods to determine the catchment area, the choice of those methods is very arbitrary. Different methods will lead to different results and their effects on the results are still not clear. This paper intends to investigate this issue by focusing on three aspects related to the catchment area: size of the catchment area, processing methods of the overlapping areas, and whether to apply the distance decay function on the catchment area. Five catchment areas are defined by drawing buffers around each station with radius distance ranging from 300 to 1500 meters with the interval of 300 meters. Three methods to process the overlapping areas are tested, which are the naïve method, Thiessen polygon, and equal division. The effect of distance decay is considered by applying lower weight to the outer catchment area. Data from five cities in the United States are analyzed. Built environment characteristics within the catchment area are extracted as explanatory variables. Annual average weekday ridership of each station is used as the response variable. To further analyze the effect of regression models on the results, three commonly used models, including the linear regression, log-linear regression, and negative binomial regression models, are applied to examine which type of catchment area yields the highest goodness-of-fit. We find that the ideal buffer sizes vary among cities, and different buffer sizes do not have a great impact on the model’s goodness-of-fit and prediction accuracy. When the catchment areas are heavily overlapping, dividing the overlapping area by the number of times of overlapping can improve model results. The application of distance decay function could barely improve the model results. The goodness-of-fit of the three models is comparable, though the log-linear regression model has the highest prediction accuracy. This study could provide useful references for researchers and planners on how to select catchment areas when constructing direct demand models for urban rail transit stations.
      PubDate: Wed, 30 Jun 2021 07:35:01 +000
       
  • The Effect of Acceptability and Personality on the Intention to Use
           Automated Vehicles among Chinese Samples

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      Abstract: The development of automated vehicles (AVs) has attracted increasing attention. Understanding public acceptance of AVs and their intention to use them, which are the primary aims of the present study, are especially important considering that increasingly more AVs will be moving on the road in the coming future. A total of 527 participants voluntarily and validly completed a series of questionnaires, including the automated vehicle acceptability scale (AVAS), Big Five Inventory (BFI), and some sociodemographic variables. The results of an internal consistency, reliability, and confirmatory factor analysis (CFA) confirmed the two-factor (contextual acceptability and impaired driving) structure of the AVAS. The Chinese public generally has a positive attitude towards AVs. In addition, our results indicate the possibility of the misuse of AVs. More importantly, the results reveal that contextual acceptability partially mediated the effect of agreeableness on the willingness to drive and the willingness to own AVs and fully mediated its effect on the willingness to rent AVs, while contextual acceptability and interest in impaired driving fully mediated the effect of the openness on the willingness to drive, own, and rent AVs. Manufacturers and retailers in the automotive vehicle industry should provide their customers with comprehensive information regarding the principles and limitations behind the system and the responsibility and obligations of the drivers to avoid misuse. Moreover, providing more targeted services according to customers’ different personality traits might be a useful sales technique.
      PubDate: Tue, 29 Jun 2021 11:35:01 +000
       
 
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