Transportation最新文献

The high cost of conventional surveys has motivated researchers to develop methods for adjusting a prior Origin–destination (OD) matrix from easily available traffic counts. The gradient method is a mathematical programming approach widely used for the OD matrix adjustment problem (ODMAP). However, this method easily gets trapped in local optima due to the non-convexity of the problem. Moreover, validation of the gradient solutions against predefined target matrices shows the method has considerable difficulty with estimating the sum of the OD matrix elements. Particle swarm optimization (PSO) is a metaheuristic which is getting lots of attention for its global search ability, but is less accurate in local search. The proposed algorithm hybridizes PSO with the gradient method, considering that the combination of good local convergence properties and effective global search makes an excellent algorithm for the ODMAP. Comparison of the results for a small and a real-life network demonstrates that the hybrid algorithm provides higher convergence properties and achieves more accurate solutions than its constituent parts working alone.

Given the rapid advancement of autonomous driving technology, discerning public willingness to pay and anticipated driving behavior for autonomous vehicles is crucial for their successful promotion, hastened adoption, and enhanced safety in forthcoming mixed autonomy traffic scenarios. This study employs a comprehensive online survey to scrutinize the factors influencing these objectives, encompassing socioeconomic and demographic elements, driving experience, and attitudes towards autonomous vehicles. Leveraging a sophisticated discrete choice model-the mixed logit model with heterogeneity in means and variances-this analysis unveils significant impacts of public social attitudes towards autonomous vehicles and current driving behavior on their willingness to pay for and behavior around autonomous vehicles. This paper’s contributions span theoretical modeling, experimental design, and estimation outcomes, offering valuable insights for policymakers and automobile manufacturers.

The mobility tool landscape continues to evolve because of shared mobility services, the prospect of automated vehicles (AVs), teleworking, and unpredicted challenges like the COVID-19 pandemic. It is critical that public and private sector actors understand how travel behavior changes due to these catalysts. A nationwide Canadian survey, called the Future Mobility in Canada Survey (FMCS), was designed to capture the complex decision-making processes of travel behavior changes and the adoption of new mobility tools by assessing individual preferences, affective motivations, and behavioral intentions. FMCS investigated four main areas: (1) respondents’ intentions to adopt AVs, (2) respondents’ use of shared forms of mobility services, (3) respondents’ recent experience with telework and their preferences towards it, and (4) respondents’ behavior across the COVID-19 pandemic for certain modes and telework. FMCS addressed these four areas by collecting responses from 5002 respondents between October and November 2021, across the five largest Canadian census metropolitan areas in terms of population (Toronto, Montréal, Vancouver, Ottawa-Gatineau, and Calgary) and Hamilton, which ranked 9th largest in 2021. This paper presents an overview of FMCS, emphasizing novel aspects of the survey design and data collection process reporting response burden and rates, while the major focus of this paper is scrutinizing the steps taken to obtain respondents’ intentions to adopt various types of AVs including shared AVs, pooled AVs, private AVs, and automated shuttle buses. The insights are relevant for other survey-based studies and are applicable for researchers who investigate adoption of new mobility tools and consequent travel behavior changes.

In the past few decades, there has been a significant increase in smartphone apps that are designed to help users optimise their daily activities. As a result, there has been a noticeable impact on travel demand. Some of these apps have evolved with the incorporation of additional functions in a gradual transformation into multi-function apps or SuperApps, thereby providing users with more integrated and personalised services for a wider range of activities. Focusing on Transport SuperApps (TSA) in Indonesia, this study aims to investigate how app usage behaviour interacts with the evolving functions of these apps over time. The study further examines the influence of personality traits, socio-demographic factors, and residential location on app usage patterns. In this study, longitudinal data on TSA usage from 2015–2022 was collected from users in four Indonesian cities. The Latent Markov (LMM) and Negative Binomial (NBM) Models were used to analyse the transition of behaviours, app types, and the number of apps used. The findings reveal that transport and shopping services are the most popular and consistently utilised services by users. The results suggest that the introduction of new services has a positive impact on the number of TSA services used. However, some services were found to be used only temporarily, primarily serving as alternatives to support users’ daily needs and desires. Initial higher service usage was observed among educated users with sociable and disorganised personalities, while discontinuation of usage is associated with older users and affluent households. Higher transition and continuation to use more services are also observed in larger cities like Jakarta compared to smaller cities like Cianjur.

This study investigates the influence of transport infrastructure, income inequality, information and communication technology on transport-oriented household expenditures. For this purpose, we chose the data set period from 1997 to 2021 for the 24 OECD countries and employed the cross-sectional auto-regressive distributed lagged (CS-ARDL) approach. Therefore, the outcomes reveal that income inequality drastically upsurges transport-oriented household expenditures, especially the pre-tax income share of the 100th percentile concentration of the super-rich. Besides, there is a convex relationship between income inequality and household expenditures. The magnitudes of information and communication technology (ICT) are negative and significant, implying that ICT reduces transport-related household expenditures by allowing them to access real-time information and choose their destination as well as travel modes from the comfort of their own homes. Also, the combined effect of income inequality and ICT reduces transport-related household expenditures in the sample countries. Thus, the findings recommend some policy implications and direct future research directions based on empirical evidence.

Micro-mobility transport modes like e-bikes and e-scooters promise higher flexibility when covering the first/last mile trip from/to the public transport stop/station to the destination point and vice-versa. However, safety concerns about riding a micro vehicle in mixed traffic limit the flexibility of shared mobility modes and make conventional ones still more attractive, e.g., private car and walking. This study investigates the effect of perceived safety in first/last mile mode choice by conducting an image-based double stated preference experiment targeted at potential micro-mobility users and developing ordinal and mixed logistic regression models. The Value-of-Safety (VoS) is introduced. It refers to the additional distance a user is willing to exchange to avoid an unsafe path. Main findings show that shared space can be a middle-ground solution, as it reports lower heterogeneity among individuals in terms of safety perceptions. The intensive use of e-scooters in mixed-traffic decreases the perceived safety of pedestrians, while e-bikers are threatened by the existence of heavy motorized traffic. Low mean VoS is also reported for e-scooters, demonstrating the unwillingness of potential micro-mobility service users to either detour or use this micro vehicle. The mean VoS of the e-bike is estimated as almost equal to that of the private car. It could be, hence, concluded that perceived safety can systematically explain the unobserved disutility of e-bikes.

The demand for public transport by tourists increases significantly in tourist-attractive destinations. This is in addition to regular passengers commuting to school and work. The level of irregular demand is influenced by several factors related to the characteristics of the day of the week, the period of the year, and the current weather. The main goal of the paper is to verify which factors most influence the irregular demand for transport in a tourist-attractive area to ensure operational planning of public passenger transport. Thanks to this, it is possible to ensure sufficient capacity and, at the same time, the efficiency of the operation of public passenger transport. The paper analyzes the main determinants of the irregular demand for regional public rail passenger transport in the High Tatras region of Slovakia. Multiple linear regressions were used to model the number of irregular passengers. The variables representing the day of the week, the attractiveness of the period, and the holiday were found to be the most significant. The variables describing the weather such as maximum daily temperature, precipitation, clouds, and wind had less influence. The obtained mathematical models for forecasting the irregular demand for public passenger transport can help optimize the timetable’s operational setting and the train sets’ size.

In making daily commuting trips, a part of travelers, which are called captive travelers, rely on one transport mode due to a lack of access or affordability to other transport modes. To account for the effect of such captive travelers on network equilibrium performances, this paper proposes a multi-modal network equilibrium (MMNE) model that accounts for the captive travelers and the correlations between modes and between routes. First, a hybrid mode choice model is developed by integrating the dogit and nested logit (NL) models. The hybrid dogit–NL (DNL) model has smaller direct and cross elasticity than the NL model, it alleviates the property of irrelevant from independent alternatives and takes the dogit and NL modal splits as bounds. Second, the path-size logit (PSL) model is adopted for predicting travelers’ route choices with overlapping routes. The DNL–PSL MMNE model is formulated as a mathematical programming problem that admits an equivalent and unique solution. Then, a partial linearization algorithm with the Barzilai–Borwein (BB) step sizes is developed. The numerical results reveal that captive travelers lead to lower sensitivity toward transport policies and may cause higher network total travel time; while the perception of mode similarity may impair the overall attractiveness of modes with a high degree of similarity. The observations indicate that to promote green transportation, policy efforts should be made to make use of or adjust the captivity structure and produce diversified perceptions of and preferences for different green transport modes. The BB step sizes are suggested for low travel demand cases when solving the combined travel choice problems. Further, extensions of the DNL model with bundle captivities are discussed. The results of the paper help improve the network equilibrium prediction and support transport policymaking.

Abstract

The COVID-19 pandemic was an unprecedented global crisis that has impacted virtually everyone. We conducted a nationwide online longitudinal survey in the United States to collect information about the shifts in travel-related behavior and attitudes before, during, and after the pandemic. The survey asked questions about commuting, long distance travel, working from home, online learning, online shopping, pandemic experiences, attitudes, and demographic information. The survey was deployed to the same respondents thrice to observe how the responses to the pandemic have evolved over time. The first wave of the survey was conducted from April 2020 to June 2021, the second wave from November 2020 to August 2021, and the third wave from October 2021 to November 2021. In total, 9,265 responses were collected in the first wave; of these, 2,877 respondents returned for the second wave and 2,728 for the third wave. All survey data are publicly available. This unique dataset can aid policy makers’ decision making in areas including transport, workforce development, and more. This article demonstrates the framework for conducting this online longitudinal survey. It details the step-by-step procedure involved in conducting the survey and in curating the data to make it representative of the national trends.

Smooth cycling can improve the competitiveness of bicycles. Understanding cycling speed variation during a trip reveals the infrastructure or situations which promote or prevent smooth cycling. However, research on this topic is still limited. This study analyses speed variation based on data collected in the Netherlands, using GPS-based devices, continuously recording geographical positions and thus the variation in speeds during trips. Linking GPS data to spatial data sources adds features that vary during the trip. Multilevel mixed-effects models were estimated to test the influence of factors at cyclist, trip and tracking point levels. Results show that individuals who prefer a high speed have a higher average personal speed. Longer trips and trips made by conventional electric bicycles and sport bicycles have a higher average trip speed. Tracking point level variables explain intra-trip cycling speed variations. Light-medium precipitation and tailwind increase cycling speed, while both uphill and downhill cycling is relatively slow. Cycling in natural and industrial areas is relatively fast. Intersections, turns and their adjacent roads decrease cycling speed. The higher the speed, the stronger the influence of infrastructure on speed. Separate bicycle infrastructure, such as bike tracks, streets and lanes, increase speed. These findings are useful in the areas of cycling safety, mode choice models and bicycle accessibility analysis. Furthermore, these findings provide additional evidence for smooth cycling infrastructure construction.