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

Autonomous vehicles (AVs) have gained great attention, and individual expectations and perceptions influence attitudes toward them. Combined with the consumer satisfaction index, a latent congruence path model is established to study how the consistency or inconsistency of individual expectations and perceptions affects people's behavior intention. SmartPLS is used to calculate the difference value. Data collected from 877 respondents in China show that the more consistent the individual's expectations and perceptions are, the more likely it is to promote the use of AVs, but this effect has an upper limit. Enjoyment plays a key role in promoting AVs. In addition, utilitarian motivation can positively influence behavior intention through hedonic motivation. The findings provide insights into understanding how individual perceptions affect people's behavior toward using AVs and developing better travel services for AVs, and more importantly, how government agencies and vehicle designers can attract more people to use AVs.

The Time Space Diagram (TSD) can abstractly represent multiple data sources and the macroscopic state of road traffic. However, the TSDs may be incomplete due to missing data, which seriously affects traffic management. Therefore, this paper proposed a Multi-Directional Recurrent Graph Convolutional Network (MDRGCN) for reconstructing TSDs and estimating missing traffic speeds given sparse data. We designed multi-directional RNN layers for scanning the TSDs from horizontal and vertical directions, which can fully exploit the contextual dependencies of the traffic information. In addition, our model includes graph convolution layers for mining potential spatial correlations in the TSDs. The performance of the model reconstructed from TSDs is validated on the NGSIM dataset. We also provided a comparison with other advanced methods, and the experimental results show that our method can perform well at both low and high missing rates, significantly outperforming the baseline methods.

Pedestrian level of service (PLOS) of sidewalks measures pedestrians’ perceived satisfaction for walking conditions. Available PLOS models consider vehicular traffic, elements of built environment and user’s perception of walking environment for PLOS assessment. The available PLOS models which use pedestrian perception on walking environment require extensive pedestrian survey and the model outcomes are subjective. Moreover these models do not consider pedestrian desires or expectations, which forms the basis of their perceived satisfaction. This work proposes a PLOS assessment framework for sidewalks which considers an exhaustive list of built and walking environment-related factors and the pedestrian perceived gap between desired and actual sidewalk conditions. The relative importance attached by pedestrians to each factor while assessing overall sidewalk condition was obtained through expert survey using Fuzzy Hierarchy Process. A framework for objectively assessing pedestrian perceived satisfaction level for different built and walking environment-related factors is also proposed in this work.

Several systems have been proposed in recent years for Advanced Traveler Information Systems (ATIS), ranging from personal (mobile application, on-board) navigation to VMS (Variable Message Signs). All these applications are essentially aimed at assisting the travelers in their travel choices by means of technologically dispatched information about traffic network structure and traffic conditions. It is well known that in ATIS applications one of the key issues to be addressed concerns the accuracy of the dispatched information, here intended as the ability of the system in supplying information that can be verified by the travelers to be correct (affected by a small error). This paper aims at proposing some analytical models able to simulate the effects of the (in)accuracy on travelers’ compliance with information.

Stochastic demands can impact the quality and feasibility of a solution. Robust solutions then become paramount. One way to achieve robustness in the Capacitated Vehicle Routing Problem with Stochastic Demands (CVRPSD) is to add a measure of the second-stage (recourse) distance to the objective function of the deterministic problem. We adopt variance as a measure of the recourse distance and propose a Mean-Variance (MV) model. To solve the model, a Hybrid Sampling-based solution approach is developed. Numerical experiments are conducted on benchmark instances and a selective waste collection system in Brazil. We compare our model with others from literature which also use a measure of the second-stage distance to attain robustness. The numerical results show that our model generates the most robust solutions. The comparison provides detailed features of each model and their advantages and disadvantages, helping decision-makers decide which model to utilize based on their different needs and priorities.

Comprehensively understanding how the policy influences the sustainability transition of the automotive industry is a critical focus in recent years. This paper focuses on the dual-credit policy, an underexplored but critical policy in China, and examines how it would influence the transition of automotive industry. To achieve this goal, an SD model is developed to simulate the effect of policy parameters on the policy objectives by considering the interaction of production, technology and market. The findings reveal that this policy can in general promote the sustainable development of the automotive industry by greatly decreasing its overall energy consumption. However, its impact on the market share of new energy vehicles is limited. Based on the results, some policy suggestions are proposed so as to improve the effectiveness of the dual-credit policy.

Lack of parking spaces at downtown hospitals causes urban problems like patient access delays and additional carbon emissions. This paper proposes a cumulative prospect theory-based shared parking space allocation model (CPT-SPSA model) to alleviate the parking difficulties. The CPT-SPSA model aims to maximize the profit of the shared parking platform under time window constraints considering the parking choice behavior of hospital parkers. To determine the parking choice behavior, cumulative prospect theory (CPT) is utilized to model parkers’ evaluation of parking lots considering individual heterogeneity. Numerical experiments indicate that the profit of the platform and the utilization rate of the shared parking spaces are improved significantly and more successful matches of shared parking supply and demand are achieved under the CPT-SPSA model. Those with different parking purposes vary in acceptance of each parking lot. The platform can earn more profit by reasonably adjusting the parking fees of the shared parking spaces.

In many urban areas and urban-rural regions, the price of electricity is not only subject to time-of-use tariffs, but also dependent on the usage purpose or service sector, significantly varying across different geographic locations or land uses. Such a spatial price difference greatly affects the charging behavior of electric vehicle drivers. This paper models and analyzes the possible impact of electricity-charging price on the routing choice and flow pattern of electric vehicles in a home-workplace commute traffic network. The particular circumstance in such a network accommodating electric vehicles is that commuters driving battery electric vehicles have opportunities to charge their vehicles at both home and workplace and take into account different electricity-charging prices in their travel choices. For the illustration and evaluation purposes, we presented a small synthetic example and proposed a convex programming model for characterizing the commute network flow pattern of electric vehicles that may be charged at home and/or workplace, encapsulating a piecewise electricity-charging cost function that implies commuters’ combined home-based and workplace-based charging behaviors. Given different electricity-charging prices at home and workplace, solving this problem requires tracking all individual paths in the network. As such, a path-based solution algorithm sketched in the disaggregated simplicial decomposition framework, with some modifications in the path generation phase and an added k-shortest path search procedure, is designed for problem solutions. We implemented the modeling and solution methods for deriving the network flow pattern of electric vehicles and comparing it with that of gasoline vehicles, as well as evaluating the network performance change caused by varying electricity-charging prices with multiple network-level and link-level evaluation matrices.

This study dwells upon the electric milk collection problem. The problem extends electric vehicle routing problem with time windows, considering multi-depot, multi-product, split deliveries, multi-compartment, fast chargers and fleet composition. The problem aims to create an effective routing decision-making system for electric trucks in transporting milk of different quality from producers (or milk collection points) in different locations to the factories. The objective of the electric milk collection problem is to minimize the sum of the total energy costs of electric trucks. We develop an adaptive general variable neighborhood search algorithm that involves several procedures having been tailored to handle specific features of the problem. We perform extensive computational experiments on real-life data to investigate the performance of the heuristics and offer particular insight. The results indicate that our algorithm successfully solves small- and large-scale instances in terms of solution quality and computational time. Our results also quantify the benefits of using fast charger types and fleet composition on the total energy costs.

This study aims to identify the impacts of the COVID-19 pandemic on school trips using a survey of 1120 students and their households in Cairo, Egypt. We found that students' age, distance to school, and school type (public or private) influence the mode used to school, the majority of parents would prefer to wait for one month or more after the lockdown before sending their children to school, and the pandemic has a significant impact on parents' evaluation of the various schooling options. Based on the responses and using different scenarios, we predicted that the original vehicle-kilometers-traveled would increase by 17% to 34%, with most of the expected modal shift occurring from large occupancy vehicles to smaller vehicles. This shift was attributed to; the distance to school, the father's occupation and education level, parents waiting more time to send children to school, and parents expecting reduced class capacity.