Autonomous bus services:current research status and future recommendations

Implementing autonomous bus services in several cities has garnered substantial research attention worldwide.However,the benefits and challenges of this emerging mode remain insufficiently understood.Consequently,VOSviewer was employed for a bibliometric analysis involving 300 publications,investigating the associations among authors,journals,and keywords.Subsequently,we comprehensively reviewed the current state of research on two topics and proposed future recommendations.Results indicate that the first document related to autonomous bus services was published in 2009.Most user attitude-related research data are obtained via questionnaires and analyzed using statistical techniques.Autonomous bus services are expected to benefit passengers regarding travel time,cost,safety,etc.,while passenger preferences are inconsistent.However,integrating the service into existing bus systems requires careful consideration of the schedule sequences.Notably,modular autonomous bus services present a new opportunity for the further optimization of bus services.In future studies,standardized data acquisition procedures should be developed to achieve comparable results.Regarding traveler choice behavior,the effect of specific autonomous bus service policies over time and the heterogeneity due to cultural or social contexts across regions should be assessed.To further promote autonomous bus services,based on fluctuating travel demands,the effects of vehicle capacity,speed,and cost of fleet composition should be evaluated comprehensively to optimize the bus network and schedule sequence.Owing to the protracted nature of the transition from conventional to fully autonomous buses,one should prioritize semi-autonomous bus services.Another essential future research direction is to integrate modular autonomous bus assembly or disassembly strategies with different fine-grained operation optimization techniques in various scenarios.

Impact of countdown signals on traffic safety and efficiency:a review and proposal

Countdown signals for motorized vehicles,which are intended to ensure safety on the road and regulate motor vehicle speed limits at road intersections,are still considered a relatively novel concept.These signals have been adopted by only a few countries,and the number of cities that use them is limited.This review aims to summarize the effects of countdown signals on traffic safety and efficiency and to determine the consistency and differences of existing research propositions on the matter.Based on the review,considerable research presents evidently different conclusions in the areas of driver red-light running and traffic safety.Particularly,some studies propose that countdown signals reinforce traffic safety,whereas others consider that such signals adversely affect traffic safety.Meanwhile,related literature provides varying conclusions on the aspect of traffic efficiency for vehicle headway.At present,the number of studies conducted regarding the driving behaviors of motorists toward countdown-signalized intersections is insufficient.Accordingly,such inadequate diversity in research causes difficulty in completely assessing the benefits and disadvantages of countdown signals.In this paper,an important future research direction on microcosmic driving psychological and physiological data combined with macro-driving behavior is proposed.

Inaugural Editorial:Digital Transportation and Safety

Transportation is essential to human life and a significant component of a modern economy and societal development.Since its first emergence approximately 5,000 years ago,transportation has experienced evolutionary changes from ancient horse-pulled wagons and dog-pulled sleds to modern airplane and high-speed trains,to future supersonic jets and space shuttles.Since the late 20th century,intelligent transportation systems(ITS)have been developed for improving transportation efficiency,alleviating traffic congestion,increasing roadway,air and sea transportation capacity,reducing energy consumption,and mitigating environmental pollution.

Bibliometric analysis and system review of vehicle routing optimization for emergency material distribution

Determining the optimal vehicle routing of emergency material distribution(VREMD)is one of the core issues of emergency management,which is strategically important to improve the effectiveness of emergency response and thus reduce the negative impact of large-scale emergency events.To summarize the latest research progress,we collected 511VREMD-related articles published from 2010 to the present from the Scopus database and conducted a bibliometric analysis using VOSviewer software.Subsequently,we cautiously selected 49 articles from these publications for system review;sorted out the latest research progress in model construction and solution algorithms;and summarized the evolution trend of keywords,research gaps,and future works.The results show that domestic scholars and research organizations held an unqualified advantage regarding the number of published papers.However,these organizations with the most publications performed poorly regarding the number of literature citations.China and the US have contributed the vast majority of the literature,and there are close collaborations between researchers from both countries.The optimization model of VREMD can be divided into single-,multi-,and joint-objective models.The shortest travel time is the most common optimization objective in the single-objective optimization model.Several scholars focus on multiobjective optimization models to consider conflicting objectives simultaneously.In recent literature,scholars have focused on the impact of uncertainty and special events(e.g.,COVID-19)on VREMD.Moreover,some scholars focus on joint optimization models to optimize vehicle routes and central locations(or material allocation)simultaneously.Solution algorithms can be divided into two primary categories,i.e.,mathematical planning methods and intelligent evolutionary algorithms.The branch and bound algorithm is the most dominant mathematical planning algorithm,while genetic algorithms and their enhancements are the most commonly used intelligent evolutionary algorithms.It is shown that the nondominated sorting genetic algorithmⅡ(NSGA-Ⅱ)can effectively solve the multiobjective model of VREMD.To further improve the algorithm’s performance,researchers have proposed improved hybrid intelligent algorithms that combine the advantages of NSGA-Ⅱand certain other algorithms.Scholars have also proposed a series of optimization algorithms for specific scenarios.With the development of new technologies and computation methods,it will be exciting to construct optimization models that consider uncertainty,heterogeneity,and temporality for large-scale real-world issues and develop generalized solution approaches rather than those applicable to specific scenarios.

A multi-dimensional robust optimization approach for cold-chain emergency medical materials dispatch under COVID-19: A case study of Hubei Province

For the optimization problem of the cold-chain emergency materials(CEM) distribution routes with multi-demand centers and soft time windows and to solve dispatching materials to medical treatment institutions in various places of the disaster areas under COVID-19, a multi-dimensional robust optimization(MRO) model was proposed, which was solved by a hybrid algorithm combined Pareto genetic algorithm and the improved grey relative analysis(IGRA). The proposed model comprehensively takes into consideration of the cost factors of the cold-chain logistics and robustness of solution with the purpose of minimizing the costs and maximizing robustness. The availability of the proposed approach and hybrid algorithm were thoroughly discussed and qualified through a real-world numerical simulation test case, which was a previous risk area located at Hubei Province. Research results show an average-cost reduction of 4.51% and a robustness increment of 11.69% in addition to consider the urgencies of demand. Consequently, not only the costs can be slightly reduced and the robustness be heightened, but also the blindness of the distribution can be avoided effectively with the demand urgency being considered. Research result indicates that when combining with the specific process of supplies dispatching in the prevention and control, the proposed approach is in a far better agreement in practice, and it could meet the diverse requirements of the emergency scenarios flexibly.

Exploring traffic safety climate with driving condition and driving behaviour:a random parameter structural equation model approach

This study aimed to explore traffic safety climate by quantifying driving conditions and driving behaviour.To achieve the objective,the random parameter structural equation model was proposed so that driver action and driving condition can address the safety climate by integrating crash features,vehicle profiles,roadway conditions and environment conditions.The geo-localized crash open data of Las Vegas metropolitan area were collected from 2014 to 2016,including 27 arterials with 16827 injury samples.By quantifying the driving conditions and driving actions,the random parameter structural equation model was built up with measurement variables and latent variables.Results revealed that the random parameter structural equation model can address traffic safety climate quantitatively,while driving conditions and driving actions were quantified and reflected by vehicles,road environment and crash features correspondingly.The findings provide potential insights for practitioners and policy makers to improve the driving environment and traffic safety culture.

Design of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment

Purpose–For the purpose of reducing the incidence of hazardous materials transport accident,eliminating the potential threats and ensuring their safety,aiming at the shortcomings in the process of current hazardous materials transportation management,this paper aims to construct the framework of hazardous materials transportation safety management system under the vehicle-infrastructure connected environment.Design/methodology/approach–The system takes the intelligent connected vehicle as the main supporter,integrating GIS,GPS,eye location,GSM,networks and database technology.Findings–By analyzing the transportation characteristics of hazardous materials,this system consists offive subsystems,which are vehicle and driver management subsystem,dangerous sources and hazardous materials management subsystem,route analysis and optimization subsystem,early warning and emergency rescue management subsystem,and basic information query subsystem.Originality/value–Hazardous materials transportation safety management system includes omnibearing real-time monitoring,timely updating of system database,real-time generation and optimization of emergency rescue route.The system can reduce the transportation cost and improve the ability of accident prevention and emergency rescue of hazardous materials.

A review of research on urban parking prediction

The rapid growth of urban traffic has intensified daily congestion,affecting both traffic flow and parking.Accurate parking prediction plays a vital role in effectively managing limited parking resources and is essential for the successful implementation of advanced intelligent systems.In an effort to comprehensively assess the latest developments in parking prediction,we curated a dataset of 639 articles spanning from 2010 to the present,using the Scopus database.Initially,we performed a bibliometric analysis utilizing VOSviewer software.These findings not only illuminate emerging trends within the parking prediction field but also provide strategic guidance for its progression.Subsequently,we categorized advancements in three focal areas:behavior prediction,demand prediction,and parking space prediction.A comprehensive overview of the present research status and future directions was then provided.The findings underscore the substantial progress achieved in current parking prediction models,achieved through diverse avenues like multi-source data integration,multi-variable feature extraction,nonlinear relationship modeling,deep learning techniques application,and ensemble model utilization.These innovative endeavors have not only pushed the theoretical boundaries of parking prediction but also significantly heightened the precision and applicability of predictive models in practical scenarios.Prospective research should explore avenues such as processing unstructured parking datasets,developing predictive models for small-scale data,mitigating noise interference in parking data,and harnessing potent platform fusion techniques.This study's significance transcends guiding and catalyzing advancement in academic and practical domains;it holds paramount relevance across academic research,technological innovation,decision-making support,business applications,and policy formulation.

A review of vehicle detection methods based on computer vision

With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure.In order to achieve faster and more accurate identification of traffic vehicles,computer vision and deep learning technology play a vital role and have made significant advancements.This study summarizes the current research status,latest findings,and future development trends of traditional detection algorithms and deep learning-based detection algorithms.Among the detection algorithms based on deep learning,this study focuses on the representative convolutional neural network models.Specifically,it examines the two-stage and one-stage detection algorithms,which have been extensively utilized in the field of intelligent transportation systems.Compared to traditional detection algorithms,deep learning-based detection algorithms can achieve higher accuracy and efficiency.The single-stage detection algorithm is more efficient for real-time detection,while the two-stage detection algorithm is more accurate than the single-stage detection algorithm.In the follow-up research,it is important to consider the balance between detection efficiency and detection accuracy.Additionally,vehicle missed detection and false detection in complex scenes,such as bad weather and vehicle overlap,should be taken into account.This will ensure better application of the research findings in engineering practice.

Choice behavior of commuters’rail transit mode during the COVID-19 pandemic based on logistic model

To understand whether commuters will take rail transit during the COVID-19 pandemic,a logistic regression model was constructed from three aspects of personal attributes,travel attributes and perception of COVID-19 based on 559 valid questionnaires.The results show that:occupation,commuting tools before the COVID-19 pandemic,walking time from residence to the nearest subway station,the possibility of being infected in private car and the possibility of being infected in public transport have significant influence on the commuters’choice of rail transit.Self-employed people and freelancers,commuters who used non-public transport before the COVID-19 pandemic,and commuters who take longer to walk from their residences to the nearest subway station are less likely to commute by rail transit during the COVID-19 pandemic.Commuters who think that the risk of being infected with the virus in public transport is higher have a lower probability of choosing rail transit.The confidence in bus/subway/taxi/taxi-hailing of commuters who do not choose to commute by rail transit during the COVID-19 pandemic is not high.The study of this paper can provide reference for the formulation of urban rail transit control measures during the COVID-19 pandemic,so as to formulate more perfect measures to ensure the safety of the returning workers.

An overview of Hadoop applications in transportation big data

As an open-source cloud computing platform,Hadoop is extensively employed in a variety of sectors because of its high dependability,high scalability,and considerable benefits in processing and analyzing massive amounts of data.Consequently,to derive valuable insights from transportation big data,it is essential to leverage the Hadoop big data platform for analysis and mining.To summarize the latest research progress on the application of Hadoop to transportation big data,we conducted a comprehensive review of 98 relevant articles published from 2012 to the present.Firstly,a bibliometric analysis was performed using VOSviewer software to identify the evolution trend of keywords.Secondly,we introduced the core components of Hadoop.Subsequently,we systematically reviewed the98 articles,identified the latest research progress,and classified the main application scenarios of Hadoop and its optimization framework.Based on our analysis,we identified the research gaps and future work in this area.Our review of the available research highlights that Hadoop has played a significant role in transportation big data research over the past decade.Specifically,the focus has been on transportation infrastructure monitoring,taxi operation management,travel feature analysis,traffic flow prediction,transportation big data analysis platform,traffic event monitoring and status discrimination,license plate recognition,and the shortest path.Additionally,the optimization framework of Hadoop has been studied in two main areas:the optimization of the computational model of Hadoop and the optimization of Hadoop combined with Spark.Several research results have been achieved in the field of transportation big data.However,there is less systematic research on the core technology of Hadoop,and the breadth and depth of the integration development of Hadoop and transportation big data are not sufficient.In the future,it is suggested that Hadoop may be combined with other big data frameworks such as Storm and Flink that process real-time data sources to improve the real-time processing and analysis of transportation big data.Simultaneously,the research on multi-source heterogeneous transportation big data is still a key focus.Improving existing big data technology to enable the analysis and even data compression of transportation big data can lead to new breakthroughs for intelligent transportation.

A review of vehicle speed control strategies

Currently,traffic problems in urban road traffic environments remain severe,traffic pollution and congestion have not been effectively improved,and traffic accidents are still frequent.Traditional traffic signal control methods have little effect on these problems.With the continuous improvement of communication technology and network connections,vehicle speed guidance provides a new idea for solving the above problems and has gradually become a popular topic in academic research.However,its generalization has shortcomings.Therefore,this paper summarizes the research on vehicle speed control strategies in urban road environments and provides suggestions for future research.In this paper,we summarize the existing research in four parts.First,we categorize existing research based on vehicle type.Second,the vehicle speed guidance problem is divided according to the problem research scene.Third,we summarize the existing literature regarding vehicle speed.Finally,we summarize the methods used for speed guidance.Through an analysis of the existing literature,it is concluded that there is a deficiency in the existing research,and suggestions for the future of vehicle speed guidance research are suggested.