Transportation Research Part A-Policy and Practice最新文献

{"title":"Inertia and shock effects in public transport: The case of metro line 6 in Santiago using smart card data","authors":"Jesper Bláfoss Ingvardson ,&nbsp;Sebastián Raveau ,&nbsp;Jaime Soza-Parra","doi":"10.1016/j.tra.2024.104352","DOIUrl":"10.1016/j.tra.2024.104352","url":null,"abstract":"<div><div>Traffic forecasting has traditionally relied solely on characteristics related to services and users. However, recent research has highlighted the importance of considering travellers’ psychological factors in explaining travel behaviour. While previous studies have incorporated the role of habits in travel choice behaviour, only a few have analysed the role of inertia and shock related to major changes in transport networks. This study contributes to previous research by revealing the changes in the behaviour of public transport passengers over time after the inauguration of a new metro line in Santiago, Chile, using large-scale revealed-preference data from automated fare collection systems. It explicitly analysed the consequences of the new metro on passenger behaviour by considering different passenger types, using a heteroskedastic mixed latent class public transport mode choice model incorporating both inertia effects resulting from habitual behaviour and shock effects resulting from a significant change to the public transport network. The results confirmed significant habitual behaviour among passengers, in that metro users tended to stick to using the metro, but bus users tended to switch to other modes. However, after the introduction of the new metro line, a significant shock effect was observed, whereby users had an increased tendency to switch to modes that improved their level of service, and this effect increased slightly in the longer term. The results highlight the importance of incorporating inertia and shock effects into behavioural studies.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104352"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Public views on legalising e-scooters: Insights from a Sydney case study","authors":"Stephen Greaves ,&nbsp;Matthew Beck ,&nbsp;Geoff Rose ,&nbsp;Melanie Crane","doi":"10.1016/j.tra.2024.104364","DOIUrl":"10.1016/j.tra.2024.104364","url":null,"abstract":"<div><div>In common with much of the world, e-scooters − stand-up scooters powered by an electric battery − have emerged onto the urban landscape in Australia promising a practical, greener and ‘fun’ form of personal mobility. However, this promise has unleashed a ‘Pandora’s box’ of legislative challenges with convoluted rules and catch-up regulations around the e-scooters themselves, where they are allowed, and who can use them. Currently, little is known about public views on e-scooters and whether current policies are meeting expectations particularly in jurisdictions where e-scooter use is severely restricted or illegal. Drawing on a survey of 1,522 residents of Greater Sydney, where use of e-scooters on public infrastructure is largely outlawed but ownership and use are growing, half of Sydneysiders indicate support for legalising e-scooters, with one-quarter opposed and one-quarter unsure. Support is marginally higher for private e-scooters, with less negativity toward them than shared e-scooters. Levels of disagreement around where they should be allowed are highest for main roads and footpaths, while levels of agreement are strongest for bicycle paths and to a lesser extent shared paths and low-speed roads, although both attract significant opposition, reflecting the ‘contested’ nature of such spaces. Strong support for helmets, insurance, licencing, and registration points toward treating them more as motorcycles/mopeds than bicycles. Support is marginal for allowing them on public transport. Market segmentation analysis unpicks some of the underlying factors around these heterogenous views, which seem to be strongly linked to age, exposure to e-scooters, perceptions of the local environment and residential location within Greater Sydney. The paper calls for a dialogue to begin around framing regulations for e-scooters and other forms of micro-mobility that not only support the advancement of sustainable transport but do so in a way which responds to public concerns and needs.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104364"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146568","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A user behavior and reinforcement learning based dynamic pricing method for idle connection time reduction at charging stations","authors":"Xizhen Zhou ,&nbsp;Yutong Cai ,&nbsp;Qiang Meng ,&nbsp;Yanjie Ji","doi":"10.1016/j.tra.2024.104358","DOIUrl":"10.1016/j.tra.2024.104358","url":null,"abstract":"<div><div>Reducing idle connection time is crucial for improving the operational efficiency of charging stations and enhancing the satisfaction of electric vehicle (EV) users. During idle connection time, EVs occupy charging spots without actively charging, thus preventing other vehicles from accessing them. However, there has been limited research focusing on how to shorten the idle connection time of charging stations. This study employed a stated preference survey to develop four separate logit models within the framework of mixed logit model that examines the impact of pricing strategies on the choice behavior of idle connection time and the underlying mechanisms behind drivers’ choice behavior in various durations of idle connection time (brief, moderate, lengthy, and extra-lengthy). Taking a charging station in Suzhou, China as a case study area, we developed a dynamic pricing algorithm based on Deep Q-Network reinforcement learning and choice behavior to minimize idle connection time and pricing at the charging station. The results indicate that factors such as pricing, location, charging start time, and the search time for available parking spaces have a significant impact on reducing idle connection time. Additionally, such impact varies across different durations of idle connection time, and the dynamic pricing strategy proves to be effective in reducing idle connection time. Compared to fixed pricing and semi-fixed pricing strategies, the dynamic pricing strategy can accommodate more potential charging demands at lower fee rates. The findings of this study offer valuable insights and a theoretical foundation for the real-time proactive management of EV charging stations.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104358"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The implications of drivers’ ride acceptance decisions on the operations of ride-sourcing platforms","authors":"Peyman Ashkrof ,&nbsp;Farnoud Ghasemi ,&nbsp;Rafał Kucharski ,&nbsp;Gonçalo Homem de Almeida Correia ,&nbsp;Oded Cats ,&nbsp;Bart van Arem","doi":"10.1016/j.tra.2024.104362","DOIUrl":"10.1016/j.tra.2024.104362","url":null,"abstract":"<div><div>As a two-sided digital platform, ride-sourcing has disruptively penetrated the mobility market. Ride-sourcing companies provide door-to-door transport services by connecting passengers with independent service suppliers labelled as “driver-partners”. Once a passenger submits a ride request, the platform attempts to match the request with a nearby available driver. Drivers have the freedom to accept or decline ride requests. The consequences of this decision, which is made at the operation level, have remained largely unknown in the literature. Using agent-based simulation modelling on the realistic case study of the city of Amsterdam, the Netherlands, we study the impacts of drivers’ ride acceptance behaviour, estimated from unique empirical data, on the ride-sourcing system where the platform applies regular and surge pricing strategies, and riders may revoke their requests and reject the received offers. Furthermore, we delve into the implications of various supply–demand intensities, a centralised fleet (i.e., mandatory acceptance on each ride request) versus a decentralised fleet (i.e., ride acceptance decision by each driver), ride acceptance rates, and surge pricing settings. We find that the ride acceptance decision of ride-sourcing drivers has far-reaching consequences for system performance in terms of passengers’ waiting time, driver’s revenue, operating costs, and profit, all of which are highly dependent on the ratio between demand and supply. As the system undergoes a transition from undersupplied (i.e., real-time demand locally exceeds available drivers) to balanced and then oversupplied state (i.e., more available drivers than real-time demand), ride acceptance decisions result in higher income inequality. A high acceptance rate among drivers may lead to more rides, but it does not necessarily increase their profit. Surge pricing is found to be asymmetrically in favour of all the parties despite adverse effects on the demand side due to higher trip fare. This study offers insights into both the aggregated and disaggregated levels of ride-sourcing system operations and outlines a series of transport policy and practice implications in cities that offer such ride-sourcing systems.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104362"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146680","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a deep-learning based gap index for addressing demand–supply interactions in ride-sourcing services","authors":"Guangtong Xu,&nbsp;Ying Lv,&nbsp;Huijun Sun,&nbsp;Xingrong Wang","doi":"10.1016/j.tra.2024.104344","DOIUrl":"10.1016/j.tra.2024.104344","url":null,"abstract":"<div><div>The imbalance between demand and supply is one of the fundamental operational bottlenecks in ride-sourcing markets. Resolving these spatial mismatches requires accurate evaluation of both the gap and the related demand and supply. However, existing studies lack efficient methods to quantify gaps arising from both demand and supply. To address these issues, the study proposes a Gap Index (GI) that illustrates the relative demand–supply gaps from a global perspective under various scales, rather than merely calculating the difference between them. Furthermore, in advance GI evaluation and forecasting depends on both demand and supply and can benefit the effectiveness and efficiency of ride-sourcing operation policy, the study thus proposes a multi-task deep learning framework based model (i.e., 4-in-1 MTF model) for gap evaluation and demand and supply forecasting simultaneously. To address the intrinsic spatiotemporal correlations between demand and supply and the impact of related external factors, the proposed 4-in-1 MTF model integrates ConvLSTM, 3D-CNN, 2D-CNN, and GRU techniques to effectively extract the information of spatial correlation and temporal order dependence of demand and supply, and make the related features (e.g., weather conditions, driving speed, POI, etc.) fed into the fusion network for accuracy improvement. Experiments based on Xiamen data show that the 4-in-1 MTF model outperforms the best benchmarks, reducing RMSE by 10.0% for demand and 25.5% for supply forecasts, and 17.9% for GI. The GI is then used to identify areas with demand–supply imbalances, demonstrating the adaptability of gap spatiotemporal evaluation and flexibility across various supply scales. The spatiotemporal distribution reveals that gaps are larger during peak hours compared to off-peak hours and are primarily concentrated on the island and coastal continent. As the supply radius expands, the difference in GI values between neighboring areas decreases, providing options to adjust pick-up distance policies to help balance gap spatial distribution. Furthermore, the GI can be extended to assist in vehicle deployment policies by quantifying shortages and surpluses of idle vehicles at the study units, offering valuable insights into the management of ride-sourcing operations.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104344"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who is willing to take transit in the future? Older adults’ perceived challenges and barriers to using public transit across Canada","authors":"Meredith Alousi-Jones ,&nbsp;Thiago Carvalho ,&nbsp;Merrina Zhang ,&nbsp;Isabella Jimenez ,&nbsp;Ahmed El-Geneidy","doi":"10.1016/j.tra.2025.104376","DOIUrl":"10.1016/j.tra.2025.104376","url":null,"abstract":"<div><div>Offering public transit services that meet the needs of older adults can contribute to their independence and well-being. Based on the Aging in Place survey conducted in March 2023 (N = 3,551), this research explores the barriers preventing older Canadians (65 and older) from using public transit in their area of residence. Specifically, we use factor and cluster analysis to identify non-transit user profiles (N = 491) based on survey participants’ perceptions of public transit and their stated willingness to use it in the next year. We find four distinct groups, including transit inclined, transit is a last resort, transit is not for now, and transit averse. Each group shows variation in the extent to which they are willing to use public transit in the future. To add nuances to our segmentation findings, we conduct a thematic analysis of an open-ended question pertaining to barriers to using public transit in each region. Access to public transit, frequency, travel time, reliability, safety, infrastructure, and convenience are defined as areas for potential improvement, though the prevalence of the concerns did vary between the non-user profiles. The findings from this research can be of interest to decision-makers and public transit agencies as accounting for the heterogeneity of non-transit users can help in directing strategies promoting public transit adoption among older adults in the future.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104376"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How would employees commute today if they had the same characteristics as employees in 1980? – Using entropy balancing to decompose changes in observed commuting mode choice over time in repeated cross-sections","authors":"Nicole Reinfeld,&nbsp;Tobias Hagen","doi":"10.1016/j.tra.2024.104370","DOIUrl":"10.1016/j.tra.2024.104370","url":null,"abstract":"<div><div>In recent decades, societal, economic, and technological developments have altered commuting behavior in many European and North American regions. Understanding the reasons for observed changes in commuting mode choice is crucial to implementing effective transport policy measures. Changes in commuting mode choice stem from either changing observable characteristics (structure of the population) or variables that are often unobserved for the researcher (such as prices or attitudes). Our study contributes to the literature by applying Entropy Balancing for the first time to decompose changes in commuting mode choice over time. We are the first to analyze data from the German Microcensus as repeated cross-sections covering every four years from 1980 to 2020 in transport research. We estimate counterfactual scenarios by comparing the population of 1980 to the balanced populations of consecutive survey waves. The “raw gap” describes changes in commuting mode choice over time in the unbalanced data. For example, the proportion of employees commuting by car increased from 58.0% in 1980 to 69.9% in 2016, resulting in a raw gap of 11.9 percentage points. Controlling for all observable characteristics (e.g., population structure) in the data explains between 3/4 and 7/8 of the raw gap. We find the increasing female labor force participation to have the highest dampening and longer distances between the residential location and the workplace to have the strongest increasing impact on the rise in commuting car share. The unexplained part (between 1/8 and 1/4) of the raw gap is due to unobservable variables (e.g., changes in supply, attitudes or (relative) costs). All these variables can (at least partly) be influenced by transport policy. Therefore, the unexplained part of the raw gap is an indicator of the scope for actions by transport policy. Sensitivity analyses support the validity of the results.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104370"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146051","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A simulation-based framework for quantifying potential demand loss due to operational constraints in automated mobility services","authors":"Serio Agriesti ,&nbsp;Claudio Roncoli ,&nbsp;Bat-hen Nahmias-Biran","doi":"10.1016/j.tra.2024.104372","DOIUrl":"10.1016/j.tra.2024.104372","url":null,"abstract":"<div><div>Automated vehicles are key to unlock a more widespread on-demand service, increasing accessibility also in peripheral areas of large cities. To evaluate how the performance of these services may affect the overall demand in return, multiple dimensions of the transport problem have to be considered. Indeed, despite people may be willing to use Automated Mobility On-Demand (i.e., generating a potential demand for the service), they may be less willing to consistently replace their other travel options if they, for example, experience high waiting times (determined by the performance of the service, i.e., the supply). In this study, we propose a simulation-based framework developed by integrating an activity-based and a dynamic traffic assignment model, designed to frame absorbed and lost demand at a disaggregated level. This allows capturing how the effects of network congestion and fleet constraints may cause a certain portion of the demand to shift to traditional modes of transportation, thus improving, for example, the accuracy of business cases for mobility service design or of hidden patterns of inequality for policymakers and public authorities.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104372"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the combined effects of major fuel technologies, eco-routing, and eco-driving for sustainable traffic decarbonization in downtown Toronto","authors":"Saba Sabet,&nbsp;Bilal Farooq","doi":"10.1016/j.tra.2025.104385","DOIUrl":"10.1016/j.tra.2025.104385","url":null,"abstract":"<div><div>As global efforts to combat climate change intensify, transitioning to sustainable transportation is crucial. This study investigates decarbonization strategies for urban traffic in downtown Toronto through microsimulation, focusing on eco-routing and eco-driving strategies, as well as the adoption of different fuel technologies: Battery Electric Vehicles (BEVs), Hybrid Electric Vehicles (HEVs), and conventional vehicles. A total of 140 scenarios are analyzed, incorporating varying levels of Connected and Automated Vehicle (CAV) penetration, anticipatory routing strategies, and driving behavior. Using transformer-based prediction models, the study evaluates Greenhouse Gas (GHG) and Nitrogen Oxides (NOx) emissions, average speed, and travel time. The findings demonstrate that 100% BEV adoption can reduce GHG emissions by 75%, but infrastructure and cost challenges persist. HEVs achieve moderate GHG reductions of 35%–40%, while e-fuels offer limited reductions of 5%. The study also highlights the role of eco-routing and eco-driving strategies in reducing emissions and improving travel time. However, it acknowledges potential unintended consequences, including modal shifts from active and public transportation to EVs, which could increase Vehicle Kilometers Traveled (VKT) and congestion, potentially offsetting some benefits of vehicle electrification. Integrating CAVs with anticipatory routing shows additional gains in reducing emissions and optimizing traffic flows. By providing a comprehensive evaluation of fuel technologies, traffic management strategies, and driving behaviors, this study offers actionable insights for policymakers to balance the benefits of electrification with its broader transportation impacts, supporting the development of sustainable urban mobility systems.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104385"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Concession revenues and delay reduction efforts: Case of U.S. Airports","authors":"Fecri Karanki ,&nbsp;Anming Zhang","doi":"10.1016/j.tra.2025.104381","DOIUrl":"10.1016/j.tra.2025.104381","url":null,"abstract":"<div><div>Delays are often considered an inherent part of airport operations, leading to significant costs in terms of lost productivity. Addressing these delays typically requires sacrificing desirable outputs in the short term. However, this approach may not be ideal for all airports, especially those that rely heavily on concession revenues, where the opportunity cost of delays may be higher. Existing research indicates a connection between delays and concession revenues, suggesting that increased delays can lead to higher non-aeronautical income. Using data from 59 U.S. airports spanning 2009 to 2018 and employing a two-stage directional distance function approach, we explore how concession revenues influence delay reduction efforts. Our findings reveal that airports with substantial concession revenue tend to put less effort into delay reduction. However, we also identified an (overall) inverse U-shaped relationship between concession revenues and delay reduction efforts. In other words, less emphasis on reducing delays initially boosts revenues, but beyond a certain point, further neglect leads to a decline in concession revenues. Furthermore, we examine how factors such as airport competition, airline concentration, legacy airline hub status, and airport size impact delay reduction efforts. Airports within 50 miles of competitors are more proactive in minimizing delays to maintain a competitive edge, while legacy airline hubs are less motivated to invest in delay reduction due to their market power. Finally, we found no significant evidence to suggest that airline concentration or airport size has a notable impact on delay management efforts.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104381"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}