Multimodal Transportation最新文献

Although mode switching caused by COVID-19 is an important travel behavior question in the Global North, countries in the Global South, particularly South Asia, have yet to adequately identify how transportation systems and travel behavior have evolved due to the pandemic. To contribute to this question, this study explored changes in the use frequency of the most common non-active sustainable alternatives in Bangladesh, namely buses and rickshaws. By emphasizing attitudes and perceptions along with sociodemographic characteristics, this study contributed to the understanding of how different factors influence individuals’ shifts in the usage frequency of buses and rickshaws in Bangladesh. Our study suggested that in general, buses have lost more ridership than rickshaws. Findings from a multivariate ordinal model suggested that the middle-income group had reduced their bus use frequency, and the low-income group had increased their rickshaw use frequency during the first wave of the pandemic. Also, there could be a possible reduction in bus use in the post-lockdown and post-pandemic periods if the risk perception continues to remain in the public consciousness. Moreover, those who cared less about the social distancing measures and had confidence in their immune system had increased their bus use frequency during the pandemic. Compared to bus use frequency, there have been fewer changes in rickshaw use frequency during the pandemic, indicating that rickshaws had become a comparatively more viable mode during the pandemic. Based on the findings, the study discussed the policy implications of keeping buses and rickshaws as viable, sustainable transport modes in the post-pandemic period.

Shared mobility services are evolving globally. However, the first-mile and last-mile (FMLM) shared-ride taxi service poses a complex problem due to its large-scale nature and mixed-type variables (numeric and categorical features). As the input size of the problem increases exponentially, the absence of a known polynomial-time algorithm further complicates the finding of an optimal solution. Consequently, exploring potential solutions becomes computationally infeasible for more significant instances. Thus, this paper proposes using the k-prototype algorithm, an unsupervised learning approach, to cluster passengers' requests for FMLM shared-ride taxi service, which can reduce the problem's complexity via feasible clustering. Notably, the k-prototype algorithm is suitable for data sets with both numeric and categorical variables. It demonstrates a promising ability to handle large data sets effectively. As presented in this paper, the FMLM shared-ride taxi service prototypes and their unique characteristics could be optimally identified using the k-prototype algorithm with the Silhouette coefficient (as a performance index). By examining an illustrative case study with ten mixed-type variables of 946 passengers' requests, the results demonstrate the effective clustering of passengers' requests into three distinct prototypes, which can be characterized uniquely based on the temporal factors (pickup time of individual requests) and trip characteristics (including traveled distance, taxi type, as well as pickup and drop-off locations) that are significant in operating a competitive shared-ride taxi service. This paper is anticipated to reveal useful practical implications for the relevant stakeholders, especially the taxi service providers, in managing the FMLM shared-ride taxi services optimally to ensure an efficient and effective operating system.

Traffic crashes are deemed a leading cause of death and injury in the United States. To improve traffic safety, historical traffic crash data are typically analyzed with a focus on factors such as roadway geometry and traffic volume. However, due to the infrequent and sporadic nature of traffic crashes, obtaining traffic safety evaluation for specific roadways requires a time and resource-intensive process, which involves extended periods of data collection and rigorous statistical reasoning. This paper explores alternative approaches, using hard-braking data collected from connected vehicles to develop a cost-efficient surrogate traffic safety measure. The geospatial correlations between hard-braking events and traffic crash locations are examined through two geospatial analysis methods: colocation analysis and network cross K-function. A case study was conducted in northern Nevada to identify hard-braking hot spots and reveal the overall cluster pattern. The colocation analysis identified that individual hard-braking events can be spatially related to crashes based on the network cross K-function result. The cases of four tracts in Reno, Nevada also demonstrate that the selection of clustering distances can influence the correlation between hard braking events and traffic crashes. This study shows the potential of using connected vehicle data to produce safety analyses for transportation networks.

Car-hailing and taxis coexist and constitute a healthy market in normal times when demand is sufficient for growing supplies. However, in a limited market influenced by disruptive issues such as COVID-19, drivers from online car-hailing and local taxi operators have been compelled to engage in competition due to the shrinking revenue. The distinct occupational characteristics and operation patterns of drivers in different groups directly influence their operational strategies (whether to operate or not), which remains an unexplored research area. To this end, this article analyzes the contrast in diverse operating indicators between the two service models before and following the outbreak of the epidemic based on a local case study in Suzhou. It establishes an income matrix for drivers in varied scenarios and employs evolutionary game theory (EGT) to dissect the dynamic operating strategies of taxi and online car-hailing drivers. Furthermore, considering the impact of disruptive issues on market demand, this study also introduces an optimized dynamic income incentive mechanism. The findings demonstrate that when disruptive issues arise and last for a considerable extended period, a 'winner-takes-all' market scenario might unfold - the potential monopoly of one service type. To circumvent this scenario, proactive human intervention can be employed at opportune moments, such as augmenting initial income, to establish the equilibrium state of ESS (1,1)—a balanced and robust coexistence of the two services. Overall, this paper provides a set of novel indicators to identify different drivers’ operation strategies, and applies EGT to analyze and estimate their operation strategies during disruptive events.

The use of public transport data has evolved rapidly over the past decades. Indeed, the availability of diverse data sources and advances in analytics have led to a greater emphasis on utilizing data to enhance public transport services. Rail transit systems have increasingly become the preferred mode of travel due to their comfort, speed, and (mostly) emission-free nature. However, persistent delays continue to be a concern. Machine learning-based prediction of transit delays is an emerging field gaining recognition. The first contribution of this paper is to illustrate how to exploit available open data to improve the prediction of rail transit delays using machine learning. Moreover, through a comparison of various well-known machine learning approaches, we show that they can yield significantly different results. Notably, the improved support vector machine method presented in this study exhibits exceptional performance and is well-suited for long-term predictions. Furthermore, we have incorporated explainable artificial intelligence techniques to identify and assess the most significant factors influencing delays. To perform experiments with the method and draw robust conclusions, three case studies featuring different rail services in major cities are provided.

Metro-based underground logistics (M-ULS) can relieve the pressure of urban traffic and avert the road congestion through coordinating with ground logistics system. A bi-level node location model is proposed based on the ground-underground logistics distribution mode. The cost of delivery and pickup from the perspective of service companies and customers are minimized as the upper and lower objectives. Since the model is belonged to NP-hard problem, the simulated annealing algorithm (SA algorithm) whose new solution generation part was improved according to the characteristics of the model is used to solve the problem. In the case analysis part, the distribution nodes and customer points close to Nanjing Metro Line 3 were selected to verify the validity of the model and algorithm. The result shows that the improved SA algorithm outperformed in taking less iteration times and obtaining better solutions. The total transportation cost of the system can be reduced by 21.7 % with the optimized node location plan, and 63.6 % of the ground transportation distance can be replaced by the underground transportation, such results are also obtained when the cargo demand increases by 1.5 times and 2 times, which verifies the advantages of M-ULS node location in reducing truck emission and total delivery cost.

Recently an increasing number of goods and services tends to be offered as a service, of which one of the most promising is mobility. The Mobility as a Service (MaaS) paradigm provides the integration of most of the mobility services available within an urban/rural area in a single subscription. The interest of Piedmont Region citizens towards MaaS is investigated through a certain number of choice situations (DCE – Discrete Choice Experiment) in which the respondent must select an alternative from the given set, based on the characteristics of each alternative. To complete the survey, a number of classic questions (multiple choice and open-ended questions) is also submitted.

The goal of the surveys is to understand the perception towards MaaS and identify the relevance of each modal alternative, to better design the composition of mobility bundles.

The research showed that Piedmont citizens are strongly interested in MaaS solutions and that bundles with highest interest include Local Public Transport (LPT) as a base, with sharing services (car, bike, scooter) in urban areas and car rental or car-sharing in suburban areas.

Determining proper locations to establish emergency response facilities is a critical strategic element of pollution preparedness and response planning for oil spills in remote areas. Many location-allocation models are available in the literature, but Arctic contexts such as remoteness and environmental sensitivities are still inadequately investigated while building optimization models. A Mixed Integer Programming (MIP) based optimization model is developed to devise a location-allocation problem: maximizing weighted spill coverage considering spill size, environmental sensitivity, and response time. Strategic decisions - e.g. allocation of stockpiling resources to resource stations and which response stations to open - are incorporated into the model as decision variables. Input parameters of the model are estimated using numerical and geospatial data of potential oil spills and response stations. The model is illustrated for hypothetical oil spill scenarios in the Canadian Arctic. The model provides optimal allocation of resources and recommends best-suited locations to build response facilities. Data visualization tools including Network Diagrams and sensitivity analysis on different model configurations, show the adequacy of the proposed mathematical modelling approach to solve the given problem. Multiple facility locations have been compared to cover all possible oil spills along Arctic shipping routes, further revealing a few better locations considering realistic constraints. Decision makers can use such optimization modelling information – e.g., how many stations to build in the Arctic to adequately cover potential oil spills – to aid strategic decision-making of maritime shipping.

Highway-rail grade crossing (HRGC) crashes in non-divided two-way traffic scenarios have caused numerous fatalities and injuries over the years. Although crucial to the safety of multimodal transportation systems, these crossings have received little attention and previous studies did not fully account for the unobserved heterogeneity and its potential interactive effects. To bridge these gaps, the HRGC crashes occurring between 2019 and 2020 in the United States were collected from the Federal Railroad Administration's Office of Safety Analysis System. A random parameters logit model with heterogeneity in means was developed to investigate the impact of multiple factors associated with crossings, crashes, drivers, vehicles, and the environment. The present study indicates that did not stop behavior generates the random parameter with heterogeneity in means that is influenced by the dark and land with commercial power indicators. Furthermore, the findings show that factors such as estimated vehicle speed > 25 MPH, train speed > 45 MPH, going around the gate, old driver, female driver, motorcycle, and the driver was in vehicle indicators would increase the likelihood of more severe injury outcomes in HRGC crashes. Notably, the adverse crossing surface and truck indicators demonstrate unexpected marginal effects by reducing the likelihood of severe injury outcomes at non-divided two-way traffic HRGCs. This study emphasizes the importance of considering unobserved heterogeneity in the context of HRGC crashes. The findings can serve as a foundation for developing targeted interventions aimed at enhancing road and railway safety.

As one of the most important modes of transportation, buses play an essential part in people's journeys. However, less has been known about whether and under what circumstances people will choose to travel by bus during the COVID-19 epidemic. Based on the theory of planned behaviour (TPB), the study examined how the impact of the different stages of COVID-19 (middle stage and later stage), the quality of public transport services and the fear of COVID-19 (FCV-19) are associated with individual choices.

An online survey of 404 participants from Anhui Province, China was conducted. We collected data on demography information, together with related TPB constructs (behavioural intentions, self-reported bus use, attitudes, subjective norm, perceived behavioural control, fear of COVID-19 and bus passenger satisfaction). After verifying the scales and the basic TPB model, we established a structural equation model (SEM).

Our analysis of the survey data yielded the following results: 1) In the middle stage of the COVID-19 epidemic, fear of COVID-19 was significantly related to the intention to take the bus, and passenger satisfaction was insignificantly related to travel by bus. 2) In the later stage of the COVID-19 epidemic, passenger satisfaction was significantly related to the decision to take the bus, and fear of COVID-19 was significantly related to the intention to travel by bus.

These research results not only reveal the internal mechanism of passengers choosing to take the bus but also provide important information for future disaster emergency management. On this basis, some feasible suggestions are made to avoid the decrease of bus ridership and help the public transportation system recover from the crisis.