Pub Date : 2025-02-01DOI: 10.1016/j.tra.2024.104330
Nicola Ortelli , Matthieu de Lapparent , Silvia F. Varotto , Michel Bierlaire
This study presents a flexible model for risk-taking behavior and accident injury severity. It is specifically designed to evaluate the impact of Via Sicura, a Swiss road safety program, on the severity of accident outcomes. Our proposed model treats the risk-taking behavior of each driver as a latent variable that depends on a number of socioeconomic and contextual factors, and whose manifestation can be measured by means of behavioral indicators. The aggregated risk, a central feature of our framework, represents the combined latent risk-taking behaviors among all drivers within an accident and is successfully identified as explanatory of the severity of injuries sustained by all individuals involved. Our findings reveal that Via Sicura’s repressive measures successfully deter risk-taking behavior among drivers, preventing an estimated 63 fatal, 876 major and 2,303 minor injuries over a ten-year period.
{"title":"An integrated ordered logit and latent variable model for accident injury severity and risk-taking behavior","authors":"Nicola Ortelli , Matthieu de Lapparent , Silvia F. Varotto , Michel Bierlaire","doi":"10.1016/j.tra.2024.104330","DOIUrl":"10.1016/j.tra.2024.104330","url":null,"abstract":"<div><div>This study presents a flexible model for risk-taking behavior and accident injury severity. It is specifically designed to evaluate the impact of Via Sicura, a Swiss road safety program, on the severity of accident outcomes. Our proposed model treats the risk-taking behavior of each driver as a latent variable that depends on a number of socioeconomic and contextual factors, and whose manifestation can be measured by means of behavioral indicators. The aggregated risk, a central feature of our framework, represents the combined latent risk-taking behaviors among all drivers within an accident and is successfully identified as explanatory of the severity of injuries sustained by all individuals involved. Our findings reveal that Via Sicura’s repressive measures successfully deter risk-taking behavior among drivers, preventing an estimated 63 fatal, 876 major and 2,303 minor injuries over a ten-year period.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104330"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146516","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2024.104300
Sander van Cranenburgh, Francisco Garrido-Valenzuela
Visual imagery is indispensable to many multi-attribute decision situations. Examples of such decision situations in travel behaviour research include residential location choices, vehicle choices, tourist destination choices, and various safety-related choices. However, current discrete choice models cannot handle image data algorithmically and thus cannot incorporate information embedded in images into their representations of choice behaviour. This gap between discrete choice models’ capabilities and the real-world behaviour it seeks to model leads to incomplete and, possibly, misleading outcomes. To solve this gap, this study proposes “Computer Vision-enriched Discrete Choice Models” (CV-DCMs). CV-DCMs can handle choice tasks involving numeric attributes and images by integrating computer vision and traditional discrete choice models. Moreover, because CV-DCMs are grounded in random utility maximisation principles, they maintain the solid behavioural foundation of traditional discrete choice models. We demonstrate the proposed CV-DCM by applying it to data obtained through a novel stated choice experiment involving residential location choices. In this experiment, respondents faced choice tasks with trade-offs between commute time, monthly housing cost and street-level conditions, presented using images. We find that CV-DCMs can offer novel insights into preferences regarding features presented in images, such as what street-level conditions people find most and least attractive and how these preferences vary across age groups.
{"title":"Computer vision-enriched discrete choice models, with an application to residential location choice","authors":"Sander van Cranenburgh, Francisco Garrido-Valenzuela","doi":"10.1016/j.tra.2024.104300","DOIUrl":"10.1016/j.tra.2024.104300","url":null,"abstract":"<div><div>Visual imagery is indispensable to many multi-attribute decision situations. Examples of such decision situations in travel behaviour research include residential location choices, vehicle choices, tourist destination choices, and various safety-related choices. However, current discrete choice models cannot handle image data algorithmically and thus cannot incorporate information embedded in images into their representations of choice behaviour. This gap between discrete choice models’ capabilities and the real-world behaviour it seeks to model leads to incomplete and, possibly, misleading outcomes. To solve this gap, this study proposes “Computer Vision-enriched Discrete Choice Models” (CV-DCMs). CV-DCMs can handle choice tasks involving numeric attributes and images by integrating computer vision and traditional discrete choice models. Moreover, because CV-DCMs are grounded in random utility maximisation principles, they maintain the solid behavioural foundation of traditional discrete choice models. We demonstrate the proposed CV-DCM by applying it to data obtained through a novel stated choice experiment involving residential location choices. In this experiment, respondents faced choice tasks with trade-offs between commute time, monthly housing cost and street-level conditions, presented using images. We find that CV-DCMs can offer novel insights into preferences regarding features presented in images, such as what street-level conditions people find most and least attractive and how these preferences vary across age groups.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104300"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146518","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2024.104366
Poonam Adsule, M. Manoj
This paper investigates the travel and charging choices of electric car users who also own a conventional-fuel vehicle. While travel behavior impacts on charging decisions have been explored in earlier studies, studies have excluded the possibility of shifting to conventional fuel vehicles by electric car users. In addition, the effects of the time spent at destinations on charging choices are also unexplored in travel behavior literature. Using a stated choice data collected in New Delhi, India, the study estimates a nested integrated choice and latent variable model to assess individuals’ choices among ‘travel according to the tour plan without charging the EV, ‘travel according to the tour plan with charging the EV’ and ‘complete the tour by shifting to conventional-fuel vehicles’. The study finds that increased tour distance leads to the choice of conventional fuel vehicle over the electric car. Besides, participation in a work tour and trip chain complexity positively affects choice of conventional fuel vehicles. While a higher initial SoC leads to decreased preferences for completing a tour on electric car with charging and shifting to conventional fuel vehicles. Overall, the study finds that travel behavior influences charging choices and travel decisions, E-car users still value conventional-fuel vehicles in accomplishing their travel patterns if E-car attributes are unfavorable.
{"title":"A stated choice study to assess charging and travel decisions of electric car users considering car attributes and trip chaining complexity – Case New Delhi, India","authors":"Poonam Adsule, M. Manoj","doi":"10.1016/j.tra.2024.104366","DOIUrl":"10.1016/j.tra.2024.104366","url":null,"abstract":"<div><div>This paper investigates the travel and charging choices of electric car users who also own a conventional-fuel vehicle. While travel behavior impacts on charging decisions have been explored in earlier studies, studies have excluded the possibility of shifting to conventional fuel vehicles by electric car users. In addition, the effects of the time spent at destinations on charging choices are also unexplored in travel behavior literature. Using a stated choice data collected in New Delhi, India, the study estimates a nested integrated choice and latent variable model to assess individuals’ choices among ‘travel according to the tour plan without charging the EV, ‘travel according to the tour plan with charging the EV’ and ‘complete the tour by shifting to conventional-fuel vehicles’. The study finds that increased tour distance leads to the choice of conventional fuel vehicle over the electric car. Besides, participation in a work tour and trip chain complexity positively affects choice of conventional fuel vehicles. While a higher initial SoC leads to decreased preferences for completing a tour on electric car with charging and shifting to conventional fuel vehicles. Overall, the study finds that travel behavior influences charging choices and travel decisions, E-car users still value conventional-fuel vehicles in accomplishing their travel patterns if E-car attributes are unfavorable.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104366"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146591","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2025.104374
JiangBo Yu , JianCheng Weng , Tian Wang , PengFei Lin , YuXing Sun , JiaoLong Chai
Airport connectivity travel is the last mile of the air travel process. The characteristics of different connectivity travel modes are key factors influencing air passengers’ choices. However, the arriving passengers’ subjective expectations and satisfaction levels differ significantly across these modes. Therefore, considering differences in satisfaction of passengers with connectivity services, the study conducts a questionnaire survey at Beijing Capital International Airport and Beijing Daxing International Airport, and 542 valid questionnaires are obtained. Arriving passengers’ satisfaction with different modes was measured based on the structural equation model. The latent profile analysis was utilized to classify into different categories considering their satisfaction levels. A new model, the scale-adjusted latent class model based on Random Regret Minimization (RRM), is established, using passengers’ satisfaction categories as a scale parameter to analyze the impact of satisfaction differences on connectivity travel mode choices. The results show that there are two primary satisfaction categories, one group has high satisfaction with metro (HSM) and the other has high satisfaction with car-hailing (HSC). Passengers’ preference heterogeneity is revealed and can be divided into three classes, avoiding-transfer (ATC), cost-sensitive (CSC), and time-sensitive (TSC). Waiting time, comfort degree, travel out-of-pocket expenses (OPE), and transfer convenience significantly influence mode choice behavior of TSC. The CSC would be more concerned about the OPE. ATC emphasizes the transfer convenience. Strategies to enhance satisfaction with connectivity services are suggested, aiming to optimize the last mile of intercity travel and promote efficient urban transportation operations.
{"title":"How do differences in airline passengers’ satisfaction with connectivity modes affect last-mile travel choices? A SALC modeling based on RRM","authors":"JiangBo Yu , JianCheng Weng , Tian Wang , PengFei Lin , YuXing Sun , JiaoLong Chai","doi":"10.1016/j.tra.2025.104374","DOIUrl":"10.1016/j.tra.2025.104374","url":null,"abstract":"<div><div>Airport connectivity travel is the last mile of the air travel process. The characteristics of different connectivity travel modes are key factors influencing air passengers’ choices. However, the arriving passengers’ subjective expectations and satisfaction levels differ significantly across these modes. Therefore, considering differences in satisfaction of passengers with connectivity services, the study conducts a questionnaire survey at Beijing Capital International Airport and Beijing Daxing International Airport, and 542 valid questionnaires are obtained. Arriving passengers’ satisfaction with different modes was measured based on the structural equation model. The latent profile analysis was utilized to classify into different categories considering their satisfaction levels. A new model, the scale-adjusted latent class model based on Random Regret Minimization (RRM), is established, using passengers’ satisfaction categories as a scale parameter to analyze the impact of satisfaction differences on connectivity travel mode choices. The results show that there are two primary satisfaction categories, one group has high satisfaction with metro (HSM) and the other has high satisfaction with car-hailing (HSC). Passengers’ preference heterogeneity is revealed and can be divided into three classes, avoiding-transfer (ATC), cost-sensitive (CSC), and time-sensitive (TSC). Waiting time, comfort degree, travel out-of-pocket expenses (OPE), and transfer convenience significantly influence mode choice behavior of TSC. The CSC would be more concerned about the OPE. ATC emphasizes the transfer convenience. Strategies to enhance satisfaction with connectivity services are suggested, aiming to optimize the last mile of intercity travel and promote efficient urban transportation operations.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104374"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146681","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2025.104378
Xianing Wang , Linjun Lu , Zhan Zhang , Ying Wang , Haoming Li
The vehicle-infrastructure cooperative control system (VICCS) leverages autonomous driving technology and interactive communication between vehicles and infrastructure to maximize system-wide benefits. As this technology emerges, a thorough socio-economic evaluation is essential to substantiate its utility. Analyzing comparisons with traditional systems will assist in adopting this innovative technology. This paper quantifies the potential benefits of the VICCS through several steps: defines the application scenarios of VICCS, models the behavioral control of vehicles and traffic signals, simulates the system in mixed-autonomy traffic environments at signalized intersections, analyzes the operational performance and service levels of VICCS, and evaluates the costs and benefits for the private and public sectors. This study employs a technical framework for VICCS that integrates deep reinforcement learning (DRL) methods to optimize vehicle speed and dynamic traffic signal control. The DRL approach is crafted to forecast the system’s performance and level of intelligence in prospective settings more accurately. The findings reveal that the anticipated VICCS will confer considerable benefits, including enhanced safety, operational efficiency, and environmental sustainability, at a cost to be incurred compared to existing systems. This will result in an annual economic gain of at least CNY10,000 (the difference between the expenditure and the gain) for the private and public sectors. This paper provides policy recommendations to support the strategic deployment of VICCS, informing stakeholders of the practical implications and facilitating the traffic system’s integration into advanced mechanisms.
{"title":"Introducing the vehicle-infrastructure cooperative control system by quantifying the benefits for the scenario of signalized intersections","authors":"Xianing Wang , Linjun Lu , Zhan Zhang , Ying Wang , Haoming Li","doi":"10.1016/j.tra.2025.104378","DOIUrl":"10.1016/j.tra.2025.104378","url":null,"abstract":"<div><div>The vehicle-infrastructure cooperative control system (VICCS) leverages autonomous driving technology and interactive communication between vehicles and infrastructure to maximize system-wide benefits. As this technology emerges, a thorough socio-economic evaluation is essential to substantiate its utility. Analyzing comparisons with traditional systems will assist in adopting this innovative technology. This paper quantifies the potential benefits of the VICCS through several steps: defines the application scenarios of VICCS, models the behavioral control of vehicles and traffic signals, simulates the system in mixed-autonomy traffic environments at signalized intersections, analyzes the operational performance and service levels of VICCS, and evaluates the costs and benefits for the private and public sectors. This study employs a technical framework for VICCS that integrates deep reinforcement learning (DRL) methods to optimize vehicle speed and dynamic traffic signal control. The DRL approach is crafted to forecast the system’s performance and level of intelligence in prospective settings more accurately. The findings reveal that the anticipated VICCS will confer considerable benefits, including enhanced safety, operational efficiency, and environmental sustainability, at a cost to be incurred compared to existing systems. This will result in an annual economic gain of at least CNY10,000 (the difference between the expenditure and the gain) for the private and public sectors. This paper provides policy recommendations to support the strategic deployment of VICCS, informing stakeholders of the practical implications and facilitating the traffic system’s integration into advanced mechanisms.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104378"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146683","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2025.104391
Rong Cao , Hongyang Chen , David Z.W. Wang
As cities grow denser, the need for sustainable urban transport solutions intensifies. Effective management of on-street parking is critical in addressing traffic congestion and promoting environmental sustainability. This study presents a machine learning model that leverages complex spatiotemporal dependencies and incorporates essential exogenous factors to accurately predict on-street parking availability. Our approach employs a combination of graph representations-static, dynamic time-warping, and hidden state-generated graphs-to capture distinct aspects of urban parking dynamics. An attention-based fusion mechanism integrates these graphs into a cohesive dynamic graph, providing a refined understanding of parking behavior. The inclusion of external temporal features through advanced gated recurrent units enhances the model’s predictive accuracy. Rigorous testing on real datasets demonstrates the model’s superior performance, achieving a mean absolute error of 0.0379 and a mean square error of 0.0067, thereby surpassing existing benchmarks. Our results highlight the model’s efficacy as a decision-support tool for urban planners and policymakers, facilitating the development of more efficient and sustainable transport systems. Additionally, the model’s interpretability and adaptability make it a valuable tool for better understanding the intricate dynamics of urban parking. We further explore the effects of prediction accuracy and the availability of predictive information on the efficiency of the parking search process, emphasizing the critical role of accurate and timely parking data in minimizing cruising time and enhancing urban mobility.
{"title":"An attention-based dynamic graph model for on-street parking availability prediction","authors":"Rong Cao , Hongyang Chen , David Z.W. Wang","doi":"10.1016/j.tra.2025.104391","DOIUrl":"10.1016/j.tra.2025.104391","url":null,"abstract":"<div><div>As cities grow denser, the need for sustainable urban transport solutions intensifies. Effective management of on-street parking is critical in addressing traffic congestion and promoting environmental sustainability. This study presents a machine learning model that leverages complex spatiotemporal dependencies and incorporates essential exogenous factors to accurately predict on-street parking availability. Our approach employs a combination of graph representations-static, dynamic time-warping, and hidden state-generated graphs-to capture distinct aspects of urban parking dynamics. An attention-based fusion mechanism integrates these graphs into a cohesive dynamic graph, providing a refined understanding of parking behavior. The inclusion of external temporal features through advanced gated recurrent units enhances the model’s predictive accuracy. Rigorous testing on real datasets demonstrates the model’s superior performance, achieving a mean absolute error of 0.0379 and a mean square error of 0.0067, thereby surpassing existing benchmarks. Our results highlight the model’s efficacy as a decision-support tool for urban planners and policymakers, facilitating the development of more efficient and sustainable transport systems. Additionally, the model’s interpretability and adaptability make it a valuable tool for better understanding the intricate dynamics of urban parking. We further explore the effects of prediction accuracy and the availability of predictive information on the efficiency of the parking search process, emphasizing the critical role of accurate and timely parking data in minimizing cruising time and enhancing urban mobility.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"193 ","pages":"Article 104391"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143152897","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2024.104351
Angela J. Haddad, Chandra R. Bhat
The COVID-19 pandemic has led to a significant shift in healthcare delivery, with telemedicine emerging as an important additional service provision channel. This study introduces a novel methodological framework, combining a multiperiod multivariate binary probit (MBP) system and a cross-sectional MBP system, to investigate telemedicine adoption trends, as well as the facilitators and deterrents of adoption. The analysis utilizes data from a three-wave COVID Future Survey (April 2020-November 2021), supplemented by population density and healthcare-related establishment data. The results reveal a generational digital divide, with older adults exhibiting lower adoption rates due to technological barriers and preferences for traditional healthcare interactions. The study also highlights the role of the presence of children, income, transportation access, employment status, and residential location characteristics in telemedicine adoption. Notably, individuals without vehicle access or living in areas with lower geographic accessibility to healthcare providers are more likely to adopt telemedicine, suggesting its potential to reduce healthcare access disparities. The analysis of telemedicine facilitators and deterrents underscores the importance of accessibility, lifestyle preferences, privacy and security issues, technological confidence, and mobility constraints. The study provides valuable insights into policy implications across the public health, telecommunication, transportation, and urban planning sectors.
{"title":"Telemedicine adoption before, during, and after COVID-19: The role of socioeconomic and built environment variables","authors":"Angela J. Haddad, Chandra R. Bhat","doi":"10.1016/j.tra.2024.104351","DOIUrl":"10.1016/j.tra.2024.104351","url":null,"abstract":"<div><div>The COVID-19 pandemic has led to a significant shift in healthcare delivery, with telemedicine emerging as an important additional service provision channel. This study introduces a novel methodological framework, combining a multiperiod multivariate binary probit (MBP) system and a cross-sectional MBP system, to investigate telemedicine adoption trends, as well as the facilitators and deterrents of adoption. The analysis utilizes data from a three-wave COVID Future Survey (April 2020-November 2021), supplemented by population density and healthcare-related establishment data. The results reveal a generational digital divide, with older adults exhibiting lower adoption rates due to technological barriers and preferences for traditional healthcare interactions. The study also highlights the role of the presence of children, income, transportation access, employment status, and residential location characteristics in telemedicine adoption. Notably, individuals without vehicle access or living in areas with lower geographic accessibility to healthcare providers are more likely to adopt telemedicine, suggesting its potential to reduce healthcare access disparities. The analysis of telemedicine facilitators and deterrents underscores the importance of accessibility, lifestyle preferences, privacy and security issues, technological confidence, and mobility constraints. The study provides valuable insights into policy implications across the public health, telecommunication, transportation, and urban planning sectors.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104351"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143145967","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2024.104367
Rajeev Ranjan Kumar , Alok Raj , J. Ajith Kumar
The adoption of electric vehicles (EV) has increased significantly in countries such as China and Norway. Yet, it is still a concern in countries such as India, where the demand for EVs is lower than that for gasoline vehicles (GV), despite the EVs’ greater long-run operational cost-effectiveness. A plausible factor is the trade-off that consumers face in an EV vs. GV purchase between EVs’ near-term higher upfront costs and their long-term lower operational costs. We investigate this by estimating the internal discount rate, which captures consumers’ preference for immediate benefits over future gains. In doing so, we invoke the “energy efficiency paradox,” which refers to consumers’ reluctance to adopt an energy-efficient technology despite its long-run cost-effectiveness. We also study the roles played by consumer inattention, risk aversion, and subjective norm in low EV uptake. We design and conduct an experimental survey of over 600 Indian consumers, and using a combination of empirical estimation techniques, we arrive at multiple results. We find that the internal discount rate is nearly 60%, suggesting that consumers underestimate future benefits from EV usage too heavily. Our results also show that an increase in the perceived annual savings by US $1,370 is highly likely to increase consumer EV adoption likelihood by 6.8% and inattentive consumers are 10.4% less likely to adopt an EV as compared to attentive consumers. Our findings contribute to a nuanced understanding of why consumers hesitate to adopt the EV and generate insights that can have prolonged implications for practitioners and academia.
{"title":"The energy efficiency paradox in electric vehicle adoption: Estimating the internal discount rate and the influences of behavioural factors","authors":"Rajeev Ranjan Kumar , Alok Raj , J. Ajith Kumar","doi":"10.1016/j.tra.2024.104367","DOIUrl":"10.1016/j.tra.2024.104367","url":null,"abstract":"<div><div>The adoption of electric vehicles (EV) has increased significantly in countries such as China and Norway. Yet, it is still a concern in countries such as India, where the demand for EVs is lower than that for gasoline vehicles (GV), despite the EVs’ greater long-run operational cost-effectiveness. A plausible factor is the trade-off that consumers face in an EV vs. GV purchase between EVs’ near-term higher upfront costs and their long-term lower operational costs. We investigate this by estimating the internal discount rate, which captures consumers’ preference for immediate benefits over future gains. In doing so, we invoke the “energy efficiency paradox,” which refers to consumers’ reluctance to adopt an energy-efficient technology despite its long-run cost-effectiveness. We also study the roles played by consumer inattention, risk aversion, and subjective norm in low EV uptake. We design and conduct an experimental survey of over 600 Indian consumers, and using a combination of empirical estimation techniques, we arrive at multiple results. We find that the internal discount rate is nearly 60%, suggesting that consumers underestimate future benefits from EV usage too heavily. Our results also show that an increase in the perceived annual savings by US $1,370 is highly likely to increase consumer EV adoption likelihood by 6.8% and inattentive consumers are 10.4% less likely to adopt an EV as compared to attentive consumers. Our findings contribute to a nuanced understanding of why consumers hesitate to adopt the EV and generate insights that can have prolonged implications for practitioners and academia.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104367"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146567","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2024.104360
Fabian Anzenhofer , Simon Schmidbaur , Robert Klein , Claudius Steinhardt
Shared Mobility-on-Demand services have the potential to contribute to a more socially and environmentally sustainable mobility provision. However, this potential may not be fully exploited due to possible conflicts with the objectives of the service providers. Thus, political discourses address regulatory instruments to influence providers’ operational planning. This paper analyzes the effects of two currently debated instruments, i.e., the introduction of a minimum pooling rate and a minimum spatial acceptance rate. Our analysis is based on mathematical optimization models that we formulate as generalizations of the selective dial-a-ride problem. More precisely, the problem is first captured by a single-period model formulation and then generalized to a multi-period horizon to implement different regulatory strategies. In a comprehensive computational study, we solve the regulated model formulations to optimality both for artificial and real-world data provided by our industry partner FLEXIBUS. We evaluate different levels of regulation for both instruments regarding their feasibility and their impact on the Shared Mobility-on-Demand system, and thereby discuss key factors, specific issues, and recommendations with regards to the practical application of regulatory instruments for public authorities that can be derived from the results of our study. Overall, our analysis recommends a multi-period application for both regulatory instruments. In this context, a moderate minimum pooling rate can enhance environmental sustainability, though it may create trade-offs with social or service provider objectives. In contrast, a minimum spatial acceptance rate shows minimal conflicts with other objectives, but requires an effective spatial partitioning approach and is sensitive to the provider’s supply–demand ratio.
{"title":"The potential of governmental regulation on shared mobility-on-demand systems","authors":"Fabian Anzenhofer , Simon Schmidbaur , Robert Klein , Claudius Steinhardt","doi":"10.1016/j.tra.2024.104360","DOIUrl":"10.1016/j.tra.2024.104360","url":null,"abstract":"<div><div>Shared Mobility-on-Demand services have the potential to contribute to a more socially and environmentally sustainable mobility provision. However, this potential may not be fully exploited due to possible conflicts with the objectives of the service providers. Thus, political discourses address regulatory instruments to influence providers’ operational planning. This paper analyzes the effects of two currently debated instruments, i.e., the introduction of a minimum pooling rate and a minimum spatial acceptance rate. Our analysis is based on mathematical optimization models that we formulate as generalizations of the selective dial-a-ride problem. More precisely, the problem is first captured by a single-period model formulation and then generalized to a multi-period horizon to implement different regulatory strategies. In a comprehensive computational study, we solve the regulated model formulations to optimality both for artificial and real-world data provided by our industry partner FLEXIBUS. We evaluate different levels of regulation for both instruments regarding their feasibility and their impact on the Shared Mobility-on-Demand system, and thereby discuss key factors, specific issues, and recommendations with regards to the practical application of regulatory instruments for public authorities that can be derived from the results of our study. Overall, our analysis recommends a multi-period application for both regulatory instruments. In this context, a moderate minimum pooling rate can enhance environmental sustainability, though it may create trade-offs with social or service provider objectives. In contrast, a minimum spatial acceptance rate shows minimal conflicts with other objectives, but requires an effective spatial partitioning approach and is sensitive to the provider’s supply–demand ratio.</div></div>","PeriodicalId":49421,"journal":{"name":"Transportation Research Part A-Policy and Practice","volume":"192 ","pages":"Article 104360"},"PeriodicalIF":6.3,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143146520","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}
Pub Date : 2025-02-01DOI: 10.1016/j.tra.2025.104395
Margareth Gutiérrez , Ricardo Hurtubia , Juan de Dios Ortúzar
We study the roles of habit, the perception of insecurity, and the built environment in the willingness to change to cycling for compulsory trips in Santiago, Chile. Data from an innovative stated choice survey to current non-users of the bicycle (who had previously declared that they might change to bicycle on their regular trips) allowed us to estimate a hybrid latent class discrete choice model incorporating habit, insecurity and having a pro-environment attitude as latent constructs. The approach allowed us to detect significant heterogeneity in behaviour depending on the city’s location and the participant’s income level. Our results confirm that trip distance plays a preponderant role in potential bicycle choice but also affects the perception of insecurity and the habit strength; in the case of shorter trips, it seems more feasible to break the habit associated with the current mode used, increasing the willingness to switch to bicycle for regular trips. Our results also confirm the importance of having cycleways available to increase the potential switch to bicycles and show the importance of having a pro-environmental attitude. Unlike previous studies, we succeeded in associating trip attributes and built environment variables among the factors explaining habit and insecurity, allowing us to examine the effects of these intangible attributes in mode switching. Finally, we estimate subjective values of time (SVT), finding significant variations by class and type of infrastructure. In particular, there is a consistently higher perceived disutility of cycling time for higher-income individuals. However, having cycling infrastructure available reduces the SVT for both classes significantly.
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