Transportation Letters-The International Journal of Transportation Research最新文献

This study characterizes lane changing behavior of drivers under differing congestion levels and identifies extreme lane changing traits using high-resolution trajectory data. Total lane change frequency exhibited a reciprocal relationship with congestion level, but the distribution of lane change per vehicle remained unchanged as congestion increased. On average, the speed of trajectories increased by 5.4 ft/s after changing a lane. However, this gain significantly diminished as congestion worsened. Further, the average speed of lane changing vehicles was 3.9 ft/s higher than those that executed no lane changes. Two metrics were employed to identify extreme lane changing behavior: critical time-to-line-crossing (TLC^c) and lane changes per unit distance. The lowest 1% TLC^c varied between 0.71–1.57 seconds. The highest 1% of lane change rates for all lane changing vehicles was 2.5 lane changes per 1,000 ft traveled. Interestingly, no drivers in thisdataset had both excessive lane changes and lane changes with low TLC^c.

This paper studies a joint multi-depot home health care and dialysis problem of routing and scheduling decisions of health specialists. The fleet consists of electric vehicles, which use both public and private charging stations. We formulate the problem as a mixed integer linear programming model. We describe a hybrid adaptive large neighborhood search (ALNS) algorithm, which integrates construction heuristic to generate initial solution and local search procedure based on variable neighborhood descent. The hybrid ALNS successfully combines existing heuristic mechanisms and introduces several new problem-specific procedures to effectively handle the complex structure of the problem. We conduct experiments on realistic benchmark instances to investigate various problem specifications, such as constructed teams, usage rate of fast and super-fast charging technologies, and public and private charging options. We analyze the performance of the hybrid ALNS and its mechanisms. The algorithm obtained good quality results on the complex optimization problem.

Railway is a fundamental transportation mode for medium and long-distance travel in China. China’s railway transport network (CRTN) has become increasingly complex. Clarifying the structure of the CRTN and its robustness to failures is important for ensuring safe operations. This paper uses train schedule data to construct a railway physical network (RPN) and a train service network (TSN), and proposes a method to simulate the CRTN change processes under different attack strategies, clarify its robustness, and identify its backbone. The results show: First, the RPN is a typical scale-free and small-world network, while the TSN presents a complex hierarchical structure; Second, the RPN is robust to random attacks but vulnerable to targeted attacks, and attacks based on the betweenness centrality as evaluated in the RPN is the most effective mode; Third, the backbone network consists 62 cities including Beijing, Tianjin, and Shijiazhuang.

Real-time logistics (RTL), which is mainly organized by crowdsourcing, has grown rapidly in recent years. Crowdsourcing riders are the main undertakers of RTL. This paper uses crowdsourcing riders’ online comments as data sources, and uses text mining techniques such as sentiment analysis and Latent Dirichlet Allocation (LDA) topic modeling to analyze the factors that bring satisfaction and dissatisfaction to riders. The research results show that in addition to basic income, riders expect the platform to provide them with better services, skills training and safety insurance before work can bring satisfaction to riders. The lack of timely information feedback on the current platform and inaccurate order matching are the reasons for the dissatisfaction of riders. Research also shows that riders can easily gain a sense of accomplishment to help others in the process of completing RTL distribution. Interactions with merchants and customers will also affect riders’ satisfaction.

Fifty years of evolution of transportation research is revisited based on bibliometric indicators of nearly 50,000 articles, the collective publication of all transportation journals. A multitude of objective indicators all consistently determined four major divisions in the field: (i) network analysis and traffic flow, (ii) economics of transportation and logistics, (iii) travel behaviour, and (iv) road safety. Trending themes of research within the abovementioned divisions respectively are: (i) macroscopic fundamental diagram and public transport network design, (ii) nil (no distinct trending topic), (iii) land-use, active transportation, residential self-selection, travel experience/satisfaction, social exclusion and transport/spatial equity, and (iv) statistical modelling of road accidents. Furthermore, clusters of research related to topics of (a) shared mobility, (b) electric mobility, and (c) autonomous mobility constitute trending topics that are each a cross between multiple divisions of the field. These outcomes document major directions to which the transportation research is headed. Additional outcome is determination of influential outsiders, seminal articles published by non-transportation journals that have proven instrumental in the development of transportation science.

Transportation sector is considered a major contributor to the release of the carbon emissions in the atmosphere. The present research explores the effect of traffic, environmental taxes and expenditures on transport-related carbon emissions. We apply a cross-sectional autoregressive distributed lags estimator for short- and long-run estimates by using panel data for 35 OECD countries. We demonstrate traffic increase transport-related carbon emissions by 14.65% on average. Transport-related carbon emissions will rise by 1.5% over the near term as a result of the combined effect rail and road-vehicles, and energy consumption. Environmental expenditures and green transportation, on the other hand, will cut transportation emissions by 21.7% and 45.20% in the short and long runs, respectively. Furthermore, the findings reveal an inverted u-shaped link between transportation-related carbon emissions and consumption. Based on real-world evidence, this study advises that some countries reduce traffic while simultaneously increasing spending on the development of environmentally friendly transportation options.

This paper proposes a research framework for investigating the travel patterns of dockless bike-sharing and accomplishing the large-scale bike rebalancing at the city level. A case study involving Shanghai combines GPS-based bike-sharing usage data and road network data. First, the spatiotemporal mobility patterns are analyzed visually; then community detection is used to divide the study area into management sub-areas according to the mobility characteristics of bike-sharing users; in addition, a clustering algorithm is used to identify virtual stations. On this basis, a heuristic algorithm is used to generate a rebalancing scheme that enables multiple visits to a given station. The results show that Shanghai can be divided into 28 bike-sharing management sub-areas. Static rebalancing based on the identified management sub-areas reduces the number and driving distance of rebalancing vehicles in use, which is a better outcome than that with a method based on administrative divisions.

Given the vital role of public transportation in major cities, understanding the influence mechanism of bikesharing use on public transportation is necessary. In this study, we adopt the propensity score matching method to analyze the influence mechanism of bikesharing on the use of public transportation based on a data set in Shanghai. We find no significant influence of bikesharing on the use-frequency of public transportation, but a significant influence on the use-duration of public transportation. A grouping model is established based on gender, physical condition, private bike ownership, private car ownership, private electric bike ownership, and educational background. It is revealed that the use of bikesharing by the group with a private bike or a private electric bike, the group without a bachelor’s degree, the group in physical condition under sub-healthy may increase the use-duration of public transportation.