Exploring the Spatial Distribution Characteristics and Correlation Factors of Wayfinding Performance on City-Scale Road Networks Based on Massive Trajectory DataRead the full article
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.
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The Impact of Pedestrian and Nonmotorized Vehicle Violations on Vehicle Emissions at Signalized Intersections in the Real World: A Case Study in Beijing
Emission around intersections has become an issue in the urban traffic network. This paper aims to investigate the impact of pedestrian and nonmotorized vehicle violations on emissions at mixed-traffic flow intersection based on the volumes of vehicles, nonmotor vehicles, and pedestrians. Also, it focuses on the arterial and collector intersections with high vehicle volume and limited space. Running red light and crossing intersection diagonally are two critical violations, accounting for 91.75% of effective violations (interference with vehicles’ operation). In this context, a violation blocking model is developed to estimate the blocking probability for each vehicle based on the volumes of pedestrians and nonmotor vehicles. The model includes two scenarios. (1) Through phase: the violation blocking model of running red light is developed based on the survival curve (the relationship between waiting time and running red light probability). (2) Left-turn phase: the violation blocking model at this phase includes two parts: (i) crossing the intersection diagonally model is developed for the first vehicle and (ii) running red light model is developed for subsequent vehicles. The existing emission model can estimate the emissions based on the blocking positions. In the case study, emissions increase with the vehicle volume approaching the saturated flow rate and the volumes of nonmotor vehicles and pedestrians increasing. Results show that the maximum emission increase of CO (carbon monoxide) for through phase and left-turn phase can reach 16.7% and 36.4%.
Analysis of Travel Mode Choice in Seoul Using an Interpretable Machine Learning Approach
Understanding choice behavior regarding travel mode is essential in forecasting travel demand. Machine learning (ML) approaches have been proposed to model mode choice behavior, and their usefulness for predicting performance has been reported. However, due to the black-box nature of ML, it is difficult to determine a suitable explanation for the relationship between the input and output variables. This paper proposes an interpretable ML approach to improve the interpretability (i.e., the degree of understanding the cause of decisions) of ML concerning travel mode choice modeling. This approach applied to national household travel survey data in Seoul. First, extreme gradient boosting (XGB) was applied to travel mode choice modeling, and the XGB outperformed the other ML models. Variable importance, variable interaction, and accumulated local effects (ALE) were measured to interpret the prediction of the best-performing XGB. The results of variable importance and interaction indicated that the correlated trip- and tour-related variables significantly influence predicting travel mode choice by the main and cross effects between them. Age and number of trips on tour were also shown to be an important variable in choosing travel mode. ALE measured the main effect of variables that have a nonlinear relation to choice probability, which cannot be observed in the conventional multinomial logit model. This information can provide interesting behavioral insights on urban mobility.
An Intelligent Framework for Analyzing the Feasible Modes of Transportation in Metropolitan Cities: A Hybrid Multicriteria Approach
The paper aims to build a hybrid personalized multicriteria model in the Indian transportation industry to identify the most feasible transport mode suitable for commuters’ customized preferences. A hybrid multicriterion model, i.e., Fuzzy Analytical Hierarchy Process (AHP), was used to compute the criteria weights, which were subsequently analyzed by three approaches, namely, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Fuzzy TOPSIS, Evaluation Based on Distance from Average Solution (EDA), and Interpretive Ranking Process (IRP). The case of an Indian metropolitan city, Hyderabad, is taken to illustrate the proposed approach. The paper highlights the following transport modes: metropolitan train (unconventional mode) and conventional modes such as the car, public bus transport, and bikes for Hyderabad. Furthermore, sensitivity analysis is performed to identify the consistency in ranking with variation in weights, and the Ensemble Ranking and transportation experts validate the rankings.
Identifying Factors Contributing to the Motorcycle Crash Severity in Pakistan
Motorcycle is a popular mode of transportation in many developing countries, including Pakistan. Since the last decade, the registered number of motorcycles in Pakistan has increased by six times, constituting 74% of the total registered vehicles. However, limited research efforts have been made to investigate motorcycle-related safety issues in Pakistan. Thus, the relationship between potential risk factors and injury outcomes of motorcycle crashes is still unclear in the country. This study, therefore, established a random parameter logit model to examine the factors associated with the motorcycle injury severity. The analysis is based on two years (2014–2015) of data collected through the road traffic injuries surveillance system from Karachi city, Pakistan. The results indicate that the summer season, weekends, nighttime, elderly riders, heavy vehicle, and single-vehicle collisions are positively associated with fatalities, while the presence of pillion passengers and motorcycle-to-motorcycle crashes are negatively associated with fatalities. More importantly, in the specific context of Pakistan, morning hours, young riders, and female pillion passengers whose clothes stuck in the wheel significantly increase the fatal injury outcomes. Based on the findings, potential countermeasures to improve motorcycle safety are discussed, such as strict enforcement to control motorcyclists' risky behavior and speeding, provision of exclusive motorcycles lanes, and education of female pillion passengers. The findings from this study would increase awareness of motorcycle safety and can be used by the policymakers to enhance road safety in Pakistan, as well as in other developing countries with similar situations.
Research on Travel Behavior with Car Sharing under Smart City Conditions
As a sustainable transportation system, car-sharing schemes have been attracting increasing attention. A large amount of research and practice has proved that the application and promotion of car sharing can help reduce the number of private cars purchased, increase the utilization rate of automobiles, effectively alleviate traffic congestion, save energy, and reduce emissions. Therefore, research on car sharing is imperative. The logit model is widely used in studies on car sharing and is an effective tool for analyzing traffic problems. This study first introduces the status of research into car sharing and analyzes the potential users and market prospects for shared cars. The study then provides the results from a questionnaire survey in Nanjing, China, to obtain sample data. Finally, a mixed logit model is established to analyze the influencing factors of car-sharing selection behavior. The results show that factors such as an individual’s housing situation and income significantly affect car-sharing decisions and that respondents who choose to use shared cars are relatively similar to commuters. The main contribution of this study is to use empirical analysis to determine the key influencing factors of car-sharing behavior in China and to provide practical insights for commercial practitioners and traffic planners.
An Improved Harmony Search Algorithm for Proactive Routing Protocol in VANET
Vehicular ad-hoc network (VANET) is the direct application of mobile ad-hoc network (MANET) in which the nodes represent vehicles moving in a city or highway scenario. The deployment of VANET relies on routing protocols to transmit the information between the nodes. Different routing protocols that have been designed for MANET were proposed to be applied in VANET. However, the real-time implementation is still facing challenges to fulfill the quality of service (QoS) of VANET. Therefore, this study mainly focuses on the well-known MANET proactive optimized link state routing (OLSR) protocol. The OLSR in VANET gives a moderate performance; this is due to its necessity of maintaining an updated routing table for all possible routes. The performance of OLSR is highly dependent on its parameter. Thus, finding optimal parameter configurations that best fit VANET features and improve its quality of services is essential before its deployment. The harmony search (HS) is an emerging metaheuristic optimization algorithm with features of simplicity and exploration efficiency. Therefore, this paper aims to propose an improved harmony search optimization (EHSO) algorithm that considers the configuration of the OLSR parameters by coupling two stages, a procedure for optimization carried out by the EHSO algorithm based on embedding two popular selection methods in its memory, namely, roulette wheel selection and tournament selection. The experimental analysis shows that the proposed approach has achieved the QoS requirement, compared to the existing algorithms.