Journal of Advanced Transportation
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Acceptance rate40%
Submission to final decision87 days
Acceptance to publication27 days
CiteScore3.700
Journal Citation Indicator0.490
Impact Factor2.249

Article of the Year 2021

A Review of Traffic Congestion Prediction Using Artificial Intelligence

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 Journal profile

Journal of Advanced Transportation publishes theoretical and innovative papers on analysis, design, operations, optimization and planning of multi-modal transport networks, transit & traffic systems, transport technology and traffic safety.

 Editor spotlight

Journal of Advanced Transportation maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors who are experts in the field of study.

 Special Issues

We currently have a number of Special Issues open for submission. Special Issues highlight emerging areas of research within a field, or provide a venue for a deeper investigation into an existing research area.

Latest Articles

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Research Article

Connected Transit Bus Dynamic Priority Weight Modeling and Conflicting Request Resolution Control at the Signalized Intersection

Multiaccess edge computing (MEC) and connected vehicle (CV) technologies have shown great potential and strength for traffic perception and real-time computing, which can be applied to enhance the efficiency of connected transit bus operations under their lower penetration conditions. Moreover, for the transit signal priority system, how to establish a model to measure traffic demand for conflicting priority request resolution and improve system response time has been widely researched for the last few decades. This paper proposes a dynamic priority weight (DPW) model for connected transit buses and a traffic signal control approach to coordinate multidirectional conflicting priority requests at a signalized intersection. The proposed model takes advantage of vehicle location, speed, and signal timing data to build time to change (TTOC) correlation functions to measure priority weights of both single-vehicle and directionality accumulation with consideration of vehicles arriving during the current green phase and conflict phase conditions; then, the aggregated priority weight value of each movement can be calculated in real-time. Once the maximum aggregated priority weight value among all movements is determined, the corresponding phase switch strategy is presented for the conflicting request resolution control problem. Homologous algorithm software for distributed deployment can be subsequently used for swift response. Simulation results show that the proposed DPW model-based traffic signal control method shows significant performance advancement, where the queueing vehicle number decrease exceeds 1 pcu/s and the throughput rate of major movements increases by approximately 2% without sacrificing the performance of minor movements in a large amount. What is more, it shows better delay optimization for social vehicles than the algorithm with delay as the objective while declining bus delay appreciable quantity with 43.4 s in average. Field test results also show that this method has excellent abilities to improve intersectional traffic capacity, for which queueing vehicle number and throughput rate indicators of all phases dramatically improved with 1.92 pcu/s and 6.68% on average, except for a slight degradation of individual minor traffic movements with 0.99 pcu/s and 0.11%.

Research Article

Flexible Scheduling Model of Bus Services between Venues of the Beijing Winter Olympic Games

Traditional buses travel on fixed routes and areas, which cannot satisfy the flexible demands of athletes in the context of COVID-19 and the closed-loop traffic management policy during the 2022 Beijing Winter Olympic Games (BWOG). This study predicts the travel demands based on the characteristics of athletes’ daily travel demands and then presents a flexible bus service scheduling model for cross-region scheduling among Beijing, Yanqing, and Zhangjiakou to provide high-level service. The flexible bus service is point-to-point and avoids unnecessary contact, which reduces the risk of spreading COVID-19 and ensures athletes’ safety. In this study, the flexible bus scheduling model is established to optimize scheduling schemes, whose object is to minimize the cost of the system based on some realistic constraints. These constraints consider not only the preferred time windows of athletes’ demand but also the vehicle’s capacity, depot, minimum load factor, total demands, etc. In addition, a genetic-simulated annealing hybrid algorithm (GSAHA) is designed to solve the model based on the characteristics of the genetic algorithm (GA) and simulated annealing. To assess the feasibility and efficiency of the model and algorithm, a case study is conducted in the Beijing-Yanqing area. Furthermore, the travel time of the flexible bus is compared to that of the traditional bus, according to the results of the case study. Moreover, the sensitivity of the model and algorithm are analyzed. The experimental results show that the proposed model and algorithm can dispatch buses with superior flexibility and high-level services during the BWOG.

Research Article

Automated Mobility-on-Demand Service Improvement Strategy through Latent Class Analysis of Stated Preference Survey

Automated driving technologies have advanced remarkably and are expected to be a part of our lives soon. Because automated driving technology does not require a driver, a significant change in future mobility services is expected. Automated driving technology is closely related to the development of public transit services as it can significantly reduce driver labor costs and provide a more comfortable in-vehicle environment. In particular, the preference for automated mobility-on-demand services that can respond in real time to the dynamic demand through automated driving technology is growing. Previous studies have compared passengers’ preferences for automated mobility-on-demand services and other transportation modes and proposed a way to enable more passengers to use automated mobility-on-demand services. However, as the number of pilot operations increases, future research will focus on ways to improve competitiveness among automated mobility-on-demand services. This study conducts a passenger preference survey based on the characteristics of automated mobility-on-demand services. In particular, changes in the in-vehicle environment and seat selection system, which differ from existing mobility-on-demand services due to automated driving technology, are investigated. The latent class modeling approach is used to classify passengers based on stated preference data collected from the survey. The estimation results show that vehicle type and seat choice system have a significant impact on passengers’ preference for automated mobility-on-demand services. In addition, considering that a high percentage of passengers do not prefer to improve autonomy in seat reservation and the in-vehicle environment, this study suggests that cost-consuming service improvement strategies are not always appropriate.

Research Article

The Longitudinal Driving Behavior of a Vehicle Assisted with Lv2 Driving Automation: An Empirical Study

As the number of automated vehicles in our transportation system increases, it becomes increasingly important to understand how automation affects their driving behavior. This study defines and tests a methodology based on optimization methods to incorporate the longitudinal driving behavior of automated vehicles in the Wiedemann 99 car-following model. A pilot study was recently conducted in Portugal using a Mercedes-Benz of 2017 assisted with level 2 driving automation to gather empirical data. In total, 61 car-following events were used to support the calibration and validation tasks. The calibration error sustains the methodology’s descriptive capability to simulate the driving behavior of AVs, and the validation error sustains that the calibrated model parameters can reproduce the dynamic driving behavior of AVs with reasonable consistency and robustness. A total of seven model parameters were estimated and are in line with the trends often described in the literature on automated vehicles but also highlight differences that can be explained by different development and deployment strategies. Nevertheless, since empirical data from automated vehicles are hard to get, the presented work findings are also valuable for improving and validating future modeling efforts.

Research Article

Estimating the Value of Statistical Life in a Road Safety Context Based on the Contingent Valuation Method

A cost-benefit analysis in a road safety context fundamentally analyzes the advantage of higher safety or lower risk. It can help determine if increasing spending on road safety programs is cost-effective. This study estimates the value of statistical life (VSL)—the amount of money that might be justified to save one person’s life. The VSL is calculated using the willingness to pay (WTP) data obtained through a contingent valuation survey. Three discrete choice models are developed: log-logistic, log-normal, and Weibull. The log-logistic model outperforms the log-normal and Weibull models, comparing Akaike’s Information Criterion (AIC) and Bayesian Information Criterion (BIC) values. We consider the log-logistic model’s mean and median WTP values to estimate the VSL value in the Ethiopian road transport safety context. The VSL estimate in the Ethiopian road transport safety context is 53.52 million ETB (USD 1.07 million). The respondents’ median WTP is ETB 714.44 (USD 14.23). Although the study is in Ethiopia, the findings can be applied to other low- and middle-income countries (LMICs) for the same purpose with modifications. The research findings will aid in a better understanding of the economic efficiencies of increased spending on road safety initiatives. Future research could compare current trends in road safety investment to the amount that should be spent based on the economic justifications from this study.

Research Article

Drivers’ Eye Movement Characteristics in a Combined Bridge-Tunnel Scenario on a Mountainous Urban Expressway: A Realistic Study

Combined bridge-tunnel scenarios of driving on mountainous city expressways occur when bridges and tunnels frequently alternate during driving. The complex nature of these driving scenarios imposes crucial requirements on the drivers’ eye movement characteristics. This paper attempts to clarify these characteristics using descriptive statistics and the box graph method, registering the pupil diameter, blink duration, fixation, saccades, and fixation loci at different tunnel locations, bridges, and ramps. Realistic driving experiments were performed on the road segment spanning from the Nanchang tunnel to the Liujiatai tunnel freeway in Chongqing, China. Eye movement data were collected for 21 drivers. The experimental results showed that, while driving in the tunnel, the maximal pupil diameter of the participating drivers was approximately 4.0 mm as the driving mileage and the number of tunnels increased, and the maximal visual load on the drivers in the tunnel tended to be stable. At the second tunnel exit, the ramp, the middle section of the first bridge, and the third tunnel exit, the driving load was the highest, while the fixation duration was shorter for nighttime driving. The fixation duration was the longest for the diversion road of bridge B1 to the ramp during the day, and the fixation times were the longest at the beginning and end of the test road. The drivers more often paid attention to the speed dashboard while entering tunnels during daytime driving (compared with nighttime driving).

Journal of Advanced Transportation
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate40%
Submission to final decision87 days
Acceptance to publication27 days
CiteScore3.700
Journal Citation Indicator0.490
Impact Factor2.249
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Article of the Year Award: Outstanding research contributions of 2021, as selected by our Chief Editors. Read the winning articles.