International Journal of Sustainable Transportation最新文献

This paper bridges the gap between the literature on the pandemic’s effects on mobility and the literature on the impact of low emission zones (LEZ). Using data for large European cities in the period 2018–2021, we examine whether LEZs may explain differences in the recovery patterns of traffic in European cities after the shock of Covid. Controlling for several city attributes, we examine whether LEZ cities are less congested before and after the pandemic in comparison to non-LEZ cities. LEZs may have been more effective in reducing congestion after the pandemic because the fleet renewal process has slowed down or, alternatively, LEZs may be a proxy of unobservable factors related with attitudes of governments and citizens toward a sustainable mobility. Our results validate the traffic-mitigating role of the LEZ after the Covid-19 pandemic, although such result only holds for the pioneering LEZ cities. Hence, the traffic-mitigating role of the LEZ after the Covid-19 pandemic seems to be related to unobservable attributes that influenced the early decision to implement a LEZ. In this regard, we also find that LEZs may have induced a change in local attributes related to sustainable mobility given that we do not find differences between LEZs decided at the local or regional level.

The structural characteristics of transportation networks (SCTN) under sustainable development goals are crucial for the development and construction of urban agglomerations (UAs). Evaluating the SCTN in UAs can be difficult due to the increasing diversity of UAs and the multiple interactive criteria involved. To address these challenges, this paper develops the G-DEMATEL model, a new data-driven model that integrates the λ-step gravity model and the decision-making trial and evaluation laboratory method. This model is then applied to various scales of Chinese UAs. The results demonstrate that: (1) three types of SCTN have developed in UAs, including polycentric, two-centers, and monocentric networks; (2) the transportation networks in UAs exhibit an excess of independent-type cities and a lack of functional differentiation among cities. (3) SCTN are significantly influenced by natural and location conditions such as geography and distance from the central city, and the gravitational effects of provincial capitals are especially significant.

Understanding the psychological factors that influence people’s behavior in using sustainable modes of transportation, e.g., public transport, is crucial for promoting environmentally friendly behavior and mitigating climate change. This study examines the impact of the big-two personality traits (stability and plasticity) on households’ auto transport consumption (ATC) and public transport consumption (PTC), and their mediating role in the relationship between socioeconomic factors (e.g., age, income, education, marital status) and transport consumptions (i.e., ATC and PTC). A triple-hurdle model, including two binary logit models and one structural equation model, is developed using a comprehensive national household survey in Australia. Findings reveal that the stability trait is positively related to ATC (b = 0.253, p < 0.10) and negatively to PTC (b = −0.372, p < 0.001), while the plasticity trait shows a positive association with PTC (b = 0.351, p < 0.001) and is negatively related to ATC (b = −0.296, p < 0.001). The developed analytical framework supports policymakers to identify individuals with stability and plasticity traits, using socioeconomic factors, and to design more-targeted interventions to incentivize specific individuals to use public transport, thus contributing to global efforts toward a sustainable future.

Traffic congestion and roadside emissions are severe and common problems in metropolitans. As a promising and sustainable solution to mitigating these vehicle externalities, shared mobility reduces the required vehicle fleet size for serving a given level of demand by sharing a vehicle among travelers with similar schedules and itineraries. Public acceptance is the key to the success of shared mobility development. This study investigates the acceptance of drivers and passengers of two typical competing shared mobility modes, car-pooling and taxi ride sharing, taking Hong Kong as a case study. For an empirical evaluation, an on-street stated preference survey was conducted, and 829 respondents, including 257 private car owners and 572 non-private car owners were interviewed about their travel preferences in three given hypothetical scenarios. In total, 2,487 observations were collected for calibrating two proposed logit-based discrete choice models for drivers and passengers. The model results show that the out-of-pocket cost, in-vehicle travel time, and out-of-vehicle time are key factors influencing travelers’ decisions toward car-pooling and taxi ride-sharing. An equilibrium model was proposed and an iteration solution procedure was applied to obtain a convergent solution to balance the demand and supply of drivers and passengers for car-pooling services. Furthermore, sensitivity analyses were carried out to examine the effects of variations in proportions of travel cost and taxi fare shared by passengers for car-pooling and taxi ride-sharing, and to assist in the formulation of relevant transport policies.

The international literature indicates a wide interest in the distances public transport users walk to access their services. Urban and transport planners seek information on acceptable walking distances (AWD) in the provision of minimum levels of service coverage. This study uses a large database from Melbourne, Australia, to analyze trip length frequency distributions (TLFD) of walking access and egress to consider AWD in a multimodal public transportation system and to examine tolerable walking distances (TWD). AWD provides a guide to planners about stop/station locations for desirable minimum service coverage. TWD is a representative maximum walking distance for assessing the effectiveness of that service provision and can be used in conjunction with AWD. A statistical distribution function for walking distances can facilitate the use of regional values for AWD and TWD in transit service planning. The Burr Type XII distribution is shown to provide a good fit to the observed data. This provides a valuable tool for the analysis of percentile walking distances and suggests a general framework for the study of AWD and TWD in any city or region.

The widening gap between real-world vehicle energy consumption and modeled predictions can be attributed to discrepancies between actual ambient temperatures and assumptions made in laboratory tests. This study collected a detailed, extensive dataset comprising 25,640,666 records of real-world vehicle operating (speed, acceleration, etc.) and fuel consumption data alongside 124,938 hourly meteorological profiles (temperature, relative humidity, etc.). High-resolution fuel consumption rates (FCRs) based on ambient temperature were developed, and adjustment factors were introduced based on ambient temperature and vehicle specific power (VSP) binning. Fuel consumption factors (FCFs) were compared across different temperatures by incorporating VSP distributions and the adjusted FCRs, revealing larger FCFs at extreme temperatures compared to moderate ones. Fuel consumption inventories, both with and without temperature adjustments, were evaluated. The results indicated a 6% underestimation of annual fuel consumption in Beijing when disregarding temperature adjustments. The variation was observed across months (in July and August, underestimations can reach 11%) and across VSP bins (larger impact in low VSP bins). The relationship between FCR and ambient temperature is similar to a quadratic curve, with the lowest consumption occurring at 10 °C–20 °C. The FCF adjustment factor does not vary across speed intervals in cold weather and remains stable at approximately 1.15 at −10 °C, but it drops from 1.25 to 1 as speed increases from 5 to 100 km/h in hot weather. This study underscores the importance of considering ambient temperature in vehicle energy consumption modeling and the necessity of temperature-adjusted approaches for accurate fuel consumption estimations.

The availability and utilization of ride-sourcing services have the potential to transform how people travel. While these services could improve mobility and accessibility, they could also attract users away from active modes and public transit and increase congestion and emissions. Understanding the impacts of transportation network companies (TNCs) on the transportation system is critical to ensure that the benefits of ride-sourcing are captured, and its negative externalities are minimized. This study uses web-based survey data administered to Metro Vancouver residents to explore the characteristics of ride-sourcing trips and the early impacts of ride-sourcing use on mode choice, given that TNCs are new to the study area. Additionally, this study utilizes stated preference experiments and error-components mixed logit models to examine the influence of sociodemographic characteristics and attitudinal factors on mode choice decisions for commuting and non-commuting trips. The results offer insights into the relationship between ride-sourcing and private vehicles, local and regional transit, taxi, and active modes (such as walking and cycling). Furthermore, model results highlight the heterogeneity in mode substitution behavior across population segments. This study can help planners and agencies capitalize on the advantages of TNCs and better integrate ride-sourcing into the transportation system.