Transportation Research Part A-Policy and Practice最新文献

Online cooked meal deliveries (CMD) have become prevalent with the advancement of on-demand delivery services offered by vendors such as Uber Eats and DoorDash. Thus, the development of a CMD demand generation model holds significant importance for CMD vendors, consumers, and policymakers. The model serves as a strategic tool for CMD vendors to address consumer needs. At the same time, it also holds substantial relevance for policymakers seeking to understand CMD demand and formulate effective regulatory measures for CMD operations. This paper presents such a modelling framework. The model is developed under the behavioural principle of random utility maximization (RUM) and explicitly represents various socioeconomic characteristics in the CMD demand generation process. The model is estimated using a Greater Toronto Area, Canada dataset. The empirical model provides insights into the factors influencing week-long CMD usage. The model also offers assessments for households’ consumer surplus brought by CMD, which can inform public policies through well-fare analysis.

Less-than-truckload (LTL) logistics presents an interesting and often overlooked practice area of hub location–allocation problems, with its variable discount rates playing a game-changing role. Vulnerability of the roads is another critical factor that has often been neglected when designing hub networks. In this paper, a novel integrated model is designed to solve the LTL hub network design problem, incorporating a practical stepwise discount function. The proposed model is further extended to a reliability-oriented version that can resiliently withstand multiple, simultaneous road disruptions. We use the failure mode and effect analysis (FMEA) technique to encompass the likelihood of experiencing each failure mode, together with the monetary and service-level effects. Coupled with probability theory, it leads to the introduction of a novel closed form for serviceability under multiple concurrent failures. Having such a function enables us to deploy exact optimization methods. Given the natural complexity of the problem, we also present effective linearization approaches. Numerical benchmarks demonstrate the superiority of the novel reliability-oriented approach over the basic version, successfully realizing desired serviceability levels as high as 80%. It also benefits from avoiding unnecessary rerouting, making it even more attractive for policymakers to design efficiently resilient transportation networks, especially if low-range, zero-emission trucks are incorporated.

This study develops a framework for mass evacuation modeling that considers staged evacuation in response to an impending hurricane for testing and evaluation. The study follows two-stage modeling processes: (1) developing a fuzzy logic-based staged evacuation model that informs staged evacuation scenario building process, and (2) developing a traffic microsimulation model to test and evaluate staged evacuation scenarios. The staged evacuation model ascertains a vulnerability-based zonal prioritization while accounting for geophysical, social, and mobility challenges utilizing the vulnerability-index. Simulation results suggest that average travel time in staged evacuation decreases by 39.5% compared to evacuation without any countermeasure applied. The staged evacuation process demonstrates a decrease of 0.3–4.8 h in clearance time for most zones. The application of vulnerability-index for prioritization enables an efficient evacuation of high-risk areas. For example, Downtown-district is prioritized due to high vulnerability-index yielding a 2.8-hour decrease in clearance time for 50% of downtown-zones. The results will help emergency professionals understand the necessity of incorporating priority needs of vulnerable populations into staged evacuation implementation, testing, and evaluation.

Previous studies with discretionary activities as the context have shown that psychological constructs (volition, desire and past behaviour) are time- and space-invariant. However, discretionary activities are less routine than people’s daily travel patterns (e.g. travel mode usage), which may result in inconsistent results among studies. The present study thus aimed to investigate the effects of temporal, social and spatial variations on the psychological mechanisms underlying more routine activity patterns such as the usage of public transport. This study was expanded to incorporate lifestyle mechanisms to determine the effects of consumption patterns on public transport. In contrast with previous studies using less routine activity patterns, on a routine basis, people’s motivation, volition and past behaviour tended to be time- and space-invariant. Indeed, the perceived ability to engage in activities was not completely predictive of behaviour; however, some spatiotemporal variables, such as mandatory and leisure activity patterns, could predict those with linear patterns of past behaviour, behaviour volition and actual behaviour. Built-environment variables (e.g. residing in areas with higher population density and being near the city centre) tend to shape public transport usage. However, shaping the built environment and having the commitment and intention to use public transport is not likely enough to shift people to use public transport. Making people loyal to public transport and focusing on those who have less commitment to perform mandatory and in-home discretionary activities are two variables that tend to shape high public transport usage.

Accurately estimating Origin–Destination matrices is a pressing challenge in transportation management and urban planning. However, traditional methods like travel surveys have limitations in availability and comprehensiveness, which have been further exacerbated by the recent changes in mobility patterns induced by the COVID-19 pandemic. To address this issue, we focused on the Trenord railway network in Lombardy, Italy, and developed an innovative pipeline to integrate ticket and subscription sales and Automated Passenger Counting data using the Iterative Proportional Fitting algorithm. By effectively navigating the complexities of diverse and incomplete data sources, our approach showcases adaptability across various transportation contexts. Our research offers a valuable tool for operators, policymakers, and researchers, bridging the gap between data availability and the need for precise OD matrices. Additionally, we emphasise the potential of dynamic OD matrices and showcase methods for detecting anomalies in mobility trends, interpreting them in the context of events from the last months of 2022.

In most European and Asian countries, the owner of the high-speed train (HST) track is either fully independent from, or integrated with, the operations of train service. In this paper, we build a simple model to analyze a particularly unique ownership structure of HST infrastructure, namely, a mixed structure featuring both integration and separation within a network. We compare this mixed case with the scenario where ownership and operations of HST are integrated and identify a conflict between social welfare (consumer surplus) and HST profit. Specifically, consumer surplus and social welfare are higher in the mixed case (compared with the integrated case), while the profit of the HST industry is lower if any of the following conditions holds: 1) the size of the market, where ownership and operations may be separated, is sufficiently small; 2) the weight that the HST operator puts on social welfare is sufficiently small; 3) the fee paid by the infrastructure owner to HST operator for operational services is sufficiently high. Our results prove to be robust when there exists competition from airlines. Additionally, we observe consistent outcomes when varying the weights on social welfare. Finally, several policy implications are discussed.

Working from home (WFH) is expected to be part of the ‘new normal’ in a post-pandemic future. WFH is often discussed as a contribution to climate protection, as WFH has the potential to reduce travel and emissions. However, there are also long-term rebound effects, which are under-investigated. These long-term effects are relevant for assessing the potential of new hybrid work concepts as a measure to improve sustainability. This paper aims to examine (1) the relationship between WFH, commuting distance and long-term effects by, (2a) the impact of WFH on the changes in commuting distance between 2011 and 2013, and (2b) the impact of commuting distance on the changes in WFH between 2011 and 2013. We used panel data from the ‘Families in Germany’ study (FiD) 2013 with changes to 2011. In this dataset, workers who WFH at least once per week had 29 % longer commuting distances than those who did not WFH. We found that the effects of WFH only show up years later when a change in commuting distance is pending, e.g., due to relocation. The change in commuting distances between 2011 and 2013 was 8.5 km higher for workers with constantly high WFH levels from 2011 to 2013 and a shift in commuting distance. Moreover, workers with a long commuting distance throughout the period tended to increase WFH between 2011 and 2013.

Existing scholarship on transportation resilience analysis has primarily focused on engineering resilience, often overlooking the intricate socio-technical dimensions. This oversight underscores the necessity for a more comprehensive understanding of the dynamic interplay between social, including travel behaviors, and technical infrastructure components within transportation systems. This article delves into the impact of “social shocks” on transportation systems, which are defined as disturbances affecting the social subsystem without yet affecting the technical subsystem. Drawing inspiration from C.S. Holling’s ecological resilience, which signifies a system’s ability to cope with change by adapting its structure and functionality, we propose a multi-level resilience assessment framework. It encompasses four mobility-related indicators: entropy (measuring network-level complexity), stationarity (assessing community compositional changes at the cluster level), and two node-level metrics — within-module degree and weighted participation coefficient — capturing location connectivity. These indicators proxy for evaluating the mobility structure and node functionality within the social subsystem. In a case study, we analyze historical smart card data to examine the mobility pattern’s structural changes within Hong Kong, a rail-oriented metropolis, during a prolonged and city-wide protest. The framework and associated indicators provide an alternative perspective for transit planners and operators, allowing them to assess both the overall system and individual stations, moving beyond traditional assessments of service supply and patronage changes.

Despite several decades of research, the relationship between transit accessibility and land values remains unclear. In practice, most research has focused on simple measures of proximity that, while easy to understand, fail to capture the potential for interaction using the transit network. Through the example of the King Street Transit Priority Corridor project, this research examines how transit accessibility, and changes in access over time that result from streetcar service upgrades, are capitalized into condominium prices in Toronto, Canada. Methodological and applied contributions include calculating streetcar travel time differences using disaggregate vehicle tracking data, calculating transit accessibility using a gravity-based measure with a calibrated impedance function, accounting for variations in accessibility over the course of a day as well as changes over time, incorporating measures of access to local amenities, transforming 2D transaction information to a 3D format, and specifying 4D spatio-temporal weights. Longitudinal model results indicate that transit accessibility is a significant determinant of condominium prices. While the service upgrades did not dramatically increase accessibility levels and the implicit value of accessibility did not change over time, panel model results find that condominium property prices appreciated by about 2.7% more on average in the King Street streetcar corridor relative to the Sheppard subway control after the introduction of the priority corridor pilot. This result suggests the corridor on the whole may have became more attractive relative to Sheppard in the pilot phase.

Most U.S. metropolitan areas developed alongside the automobile, producing neighborhoods of relatively low density. Consequently, access to opportunities in these neighborhoods is predicated on having an automobile, yet many households do not have the resources to purchase one outright, relying on automobile loans to spread out the purchase price. While automobile loans can enable automobile ownership, they also significantly increase the vehicle purchase price, particularly for non-white consumers subject to discriminatory lending practices.

In this study, we rely on data from the University of California Consumer Credit Panel from Experian to examine the determinants and geography of automobile debt and its consequences in California, testing whether various automobile debt measures disproportionately affect non-white neighborhoods.

We find that, controlling for other factors associated with automobile lending including income, Black and Latino/a neighborhoods have higher total automobile debt, debt burdens (debt relative to income), and automobile loan delinquency rates. In particular, Latino/a neighborhoods shoulder significant automobile debt, while borrowers in Black neighborhoods have the highest delinquency rates. Factors associated with lower total automobile debt and automobile debt burden include better credit ratings, higher residential densities, urban locations, and proximity to rail stations.

The findings underscore the importance of policies to offset the costs of automobile ownership and access. As part of this, policymakers should adopt and enforce fair lending rules to combat discriminatory and predatory practices and facilitate access to high-quality financial institutions and products in communities of color.