Journal of Advanced Transportation
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 Journal metrics
Acceptance rate36%
Submission to final decision106 days
Acceptance to publication75 days
CiteScore3.400
Journal Citation Indicator0.520
Impact Factor2.419

Article of the Year 2020

Identifying Big Five Personality Traits through Controller Area Network Bus Data

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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

Chief Editor, Dr Gonçalo Homem de Almeida Correia, is based at Delft University of Technology, The Netherlands. His main research interest is in the planning and operations of transport systems in urban environments.

 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

Effectiveness and Optimal Location of Real-Time Traffic Conflict Risk Warning System for Rural Unsignalized Intersections: A Driving Simulation Study

The real-time traffic conflict risk warning system (RTCRWS) is proposed as a new proactive crash prevention and control strategy for intersections designed to reduce traffic on the main road to rural unsignalized intersections when a vehicle enters the access roads. This study aims at evaluating the effectiveness of the RTCRWS with different locations based on a driving simulation experiment. In this study, four types of the RTCRWS installation location schemes (i.e., no installation, 50 m/100 m/150 m away from the unsignalized intersection) are designed. Twenty-two experienced drivers participated in the driving simulation experiment, and seven evaluating indicators representing driving behavior data are proposed. Two methods to analyze the data are applied: (1) descriptive analysis of driving behavior characteristics different location schemes of the RTCRWS and (2) entropy weight-fuzzy comprehensive evaluation of the RTCRWS. The results show that the RTCRWS has a significant effect on slowing vehicles when approaching the rural unsignalized intersections. If the location of the RTCRWS is 50 m, 100 m, and 150 m from the intersection, the comprehensive score of fuzzy evaluation is 75.82, 74.91, and 77.22, respectively, which implies that the scheme with the RTCRWS 150 m ahead of the intersection is the most effective.

Research Article

Advanced Phasmatodea Population Evolution Algorithm for Capacitated Vehicle Routing Problem

Capacitated Vehicle Routing Problem (CVRP) is difficult to solve by the traditional precise methods in the transportation area. The metaheuristic algorithm is often used to solve CVRP and can obtain approximate optimal solutions. Phasmatodea population evolution algorithm (PPE) is a recently proposed metaheuristic algorithm. Given the shortcomings of PPE, such as its low convergence precision, its nature to fall into local optima easily, and it being time-consuming, we propose an advanced Phasmatodea population evolution algorithm (APPE). In APPE, we delete competition, delete conditional acceptance and correspondingevolutionary trend update, and add jump mechanism, history-based searching, and population closing moving. Deleting competition and conditional acceptance and correspondingevolutionary trend update can shorten PPE running time. Adding a jump mechanism makes PPE more likely to jump out of the local optimum. Adding history-based searching and population closing moving improves PPE’s convergence accuracy. Then, we test APPE by CEC2013. We compare the proposed APPE with differential evolution (DE), sparrow search algorithm (SSA), Harris Hawk optimization (HHO), and PPE. Experiment results show that APPE has higher convergence accuracy and shorter running time. Finally, APPE also is applied to solve CVRP. From the test results of the instances, APPE is more suitable to solve CVRP.

Research Article

Spatiotemporal Traffic Density Estimation Based on ADAS Probe Data

This study aims to develop a spatiotemporal traffic density estimation method based on the advanced driver assistance system (ADAS) Probe data. This study uses the vehicle trajectory data collected from the ADAS equipped on the sample probe vehicles. Such vehicle trajectory data are used firstly to estimate the distance headway between the vehicles on a specific road section, and the postprocessed distance headway data are finally used to estimate the spatiotemporal traffic density. The innovation aspect of the proposed methodology in this study is that traffic density can be estimated in high accuracy only with a small size of data points in support of ADAS. On the other hand, existing density estimation method requires a large number of probe vehicles and its numerous data sets including either the global positioning system data or the dedicated short-range communication data. To verify the proposed methodology, a two-step evaluation is performed: the first step is a numerical evaluation that estimates the spatiotemporal traffic density based on the simulated vehicle trajectory data, and the second step is an empirical evaluation that estimates the density based on the real-road data in both peak and nonpeak periods. Beyond the methodology development, this study verified the estimation reliability of traffic density under various traffic conditions based on the sampling rate of ADAS-equipped vehicles. Consequently, the traffic density estimation error decreased as the sampling rate increased. Estimation accuracy of 90% or higher was observed in all scenarios when the sampling rate was 50% or higher. It indicates that fairly accurate traffic density estimation is feasible using probe vehicles that correspond to half of the vehicles driven on the road. Therefore, this practical approach is expected to mitigate the burden of density estimation, particularly in future road systems in which ADAS and autonomous vehicles are prevalent.

Research Article

Active Equalization Strategy for Lithium-Ion Battery Packs Based on Multilayer Dual Interleaved Inductor Circuits in Electric Vehicles

Lithium-ion batteries (LIBs) are widely used in electric vehicles (EVs) due to their superior power performance over other batteries. However, when connected in series, overcharged cells of LIBs face the risk of explosion, and undercharged cells decrease the life cycle of the battery. Eventually, the inconsistency phenomenon between cells resulting from manufacturing tolerance and usage process reduces the overall charging capacity of the battery and increases the risk of explosion after long-time use. Research has focused on synthesizing active material to achieve higher energy density and extended life cycle for LIBs while neglecting a comparative analysis of equalization technology on the performance of battery packs. In this paper, a nondissipative equalization structure is proposed to reconcile the inconsistency of series-connected LIB cells. In this structure, a circuit uses high-level equalization units to enable direct energy transfer between any two individual cells, and dual interleaved inductors in each equalization unit increase the equalization speed of a single cell in one equalization cycle by a factor of two. The circuit is compared with the classical inductor equalization circuit (CIEC), dual interleaved equalization circuit (DIEC), and parallel architecture equalization circuit (PAEC) in the states of standing, charging, and discharging, respectively, to validate the advantages of the proposed scheme. Considering the diversity of imbalance states, the state of charge (SOC) and terminal voltage are both chosen as the equalization criterion. The second-order RC model of the LIB and the adaptive unscented Kalman filter (AUKF) algorithm are employed for SOC estimation. For effective equalization, the adaptive fuzzy neural network (AFNN) is utilized to further reduce energy consumption and equalization time. The experiment results show that the AFNN algorithm reduces the total equalization time by approximately 37.4% and improves equalization efficiency by about 4.89% in contrast with the conventional mean-difference algorithm. Particularly, the experiment results of the equalization circuit verification certify that the proposed equalization structure can greatly accelerate the equalization progress and reduce the equalization loss compared to the other three equalization circuits.

Research Article

Investigating Contributing Factors on Aggressive Driving Based on a Structural Equation Model

Tailored countermeasures that may significantly improve road traffic safety can be proposed and implemented if the relationship between various associated factors and aggressive driving is well understood. However, this relationship remains unknown, as driving behavior is complex, and the interrelationships among variables are not easy to identify. Considering this situation, this paper constructed a model based on a structural equation model (SEM) and factor analysis (FA), which is a multivariate statistical analysis technique used to analyze structural relationships. The model is applied in a case study using data from the Shanghai Naturalistic Driving Study. In the case study, 16 variables were grouped into five latent factors in the SEM, and the model fits the data well. Compared with other variables, the results show that age had the most significant positive impact on aggressive driving behavior (older drivers exhibited high aggressive driving frequency). Adverse weather negatively impacted driver behavior (lower speed and high longitude acceleration), which in turn negatively affected aggressive driving behavior. In addition, the results show that driver factors (such as age and sex) were the main factors influencing vehicle use (such as hard acceleration), and the environment was the main factor determining risky scenarios, where safety-critical situations increase. This paper provides a reference for defining and determining aggressive driving and a model for exploring the relationship between driving safety factors and aggressive driving, which can be used in real-world applications for improving driving safety with applications in advanced driver-assistance (ADAS) and traffic enforcement safety control systems.

Research Article

How Snowfalls Affect the Operation of Taxi Fleets? A Case Study from Harbin, China

Taxi network plays an important role in urban passenger transportation. However, its operation is greatly affected by weather, especially by snowfalls in cold region. In this study, we focus on the persistent effect of snowfall on taxi operation and propose an autoregressive distribution lag model (ARDL) to quantitatively analyse it. To support our study, the taxi GPS trajectory data collected in Harbin, China, during 61 days from 1 November to 31 December in 2015 is analysed. First, the daily average order volume (DAOV) is acquired through data sampling and processing. Then, combined with the data of daily snowfall during the 61 days, the ARDL model is constructed. The result shows that the snowfall has a lag effect on taxi operation and it lasts about 3 days. To better interpret the result, visualization of total 6 days before and after a heavy snowfall is conducted. The result also indicates that weekends have a positive effect on operation. These results are expected to assist us to better understand the effect of snowfall on taxi operation and provide some policy suggestions for local municipal and transportation management departments to ensure the normal operation of taxi networks.

Journal of Advanced Transportation
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
Acceptance rate36%
Submission to final decision106 days
Acceptance to publication75 days
CiteScore3.400
Journal Citation Indicator0.520
Impact Factor2.419
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Article of the Year Award: Outstanding research contributions of 2020, as selected by our Chief Editors. Read the winning articles.