Space–time prism and accessibility incorporating monetary budget and Mobility-as-a-Service

Recent years in time geography have witnessed a flourishment of space–time prism (STP) modeling extensions for enhancing realism. However, there is little research on the incorporation of monetary budget in STP models to capture personal potential mobility space more realistically. This study considers both time and monetary budget constraints in STP modeling in a multimodal supernetwork integrating mobility-as-a-service and trip chains. We develop an efficient two-stage bi-criterion bidirectional search method to identify Pareto path sets to construct the resulting STP for conducting a flexible activity between two anchor nodes. To demonstrate the effectiveness of the proposed model and solution method, a case study with varying scenarios is conducted to evaluate the impacts of monetary budget on space–time accessibility and equality. The findings show that the ignorance of monetary budget overestimates accessibility and that MaaS, if not well designed, may not improve equality in accessibility as intended.</p

A novel two-stage approach for energy-efficient timetabling for an urban rail transit network

Urban rail transit (URT) is the backbone transport mode in metropolitan areas to accommodate large travel demands. The high energy consumption of URT becomes a hotspot problem due to the ever-increasing operation mileages and pressing agendas of carbon neutralization. The high model complexity and inconsistency in the objectives of minimizing passenger travel time and operational energy consumption are the main challenges for energy-efficient timetabling for a URT network with multiple interlinked lines. This study proposes a general model framework of timetabling and passenger path choice in a URT network to minimize energy consumption under passenger travel time constraints. To obtain satisfactory energy-efficient nonuniform timetables, we suggest a novel model reformulation as a tree knapsack problem to determine train running times by a pseudo-polynomial dynamic programming algorithm in the first stage. Furthermore, a heuristic sequencing method is developed to determine nonuniform headways and dwell times in the second stage. The suggested model framework and solution algorithm are tested using a real-world URT network, and the results show that energy consumption can be considerably reduced given certain travel time increments

Travel preferences for electric sharing mobility services:Results from stated preference experiments in four European countries

Electric sharing mobility services (ESMS) are gaining popularity as a promising solution for green transport. For sustainable mobility planning, it is important to understand the factors affecting the use behavior of ESMS and the substitution patterns of conventional transport modes. To that end, we carried out a stated preference experiment to elicit travel preference toward ESMS considering various alternatives, contexts, and traveler characteristics. Results from a scaled error component model applied to a large sample of respondents from four European countries (France, Italy, Netherlands, and Spain) show that ESMS have the potential to reduce dependency on private cars. While heterogeneity is found across countries, people at young ages, highly educated, with high income, and living in city centers are commonly associated with a higher probability of adopting ESMS for urban mobility. The substitution patterns reveal a relatively lower preference for ESMS from private car users compared to users of public transport and active modes. Operational implications are discussed for sharing mobility planners and operators to avoid unintended substitution effects

Incorporating personality traits for the study of user acceptance of electric micromobility-sharing services

Electric micromobility-sharing services (EMS) have emerged as a promising mobility tool for tackling transportation problems. Understanding the drivers of user acceptance of EMS is essential for proper deployment. However, there is no consensus in the literature on the effects of psychological factors on EMS adoption, and little research has considered personality traits to capture individual differences. To fill this research gap, we administered a survey through a Dutch panel that integrated the Big Five personality traits into a user acceptance framework and applied structural equation modeling (SEM) to investigate user acceptance of EMS. The quantitative analysis reveals that three UTAUT factors (social influence, performance expectancy, and hedonic motivation) have strong positive direct effects on user acceptance. Among the Big Five personality traits, openness and extraversion have significant but weaker total effects, while other personality traits (conscientiousness, agreeableness, and neuroticism) have no significant effects. It is also found that young people and residents of large cities have a higher intention to adopt EMS, while the majority who are highly satisfied with the status quo transportation modes have a lower intention to use EMS for short trips. The analysis results offer crucial insights into crafting tailored strategies to deploy EMS

Transitional behavioral intention to use autonomous electric car-sharing services: Evidence from four European countries

Electric car-sharing services (ECS) have been promoted as a solution to combat negative urban mobility externalities and are expected to be facilitated by fleets of autonomous vehicles. There is little evidence regarding the behavioral intention to use autonomous ECS (AECS), especially on the transition from using ECS. This paper investigates the behavioral intention to use AECS using psychological constructs partially from the extended unified theory of acceptance and use of technology (UTAUT2) and an additional one expressing safety concern. A novel behavioral intention model is presented to capture the transitional behavioral intention to use two adjacent generations of sharing mobility services. Results of structural equation models applied to a survey sample of 2154 respondents from France, Italy, Netherlands, and Spain show that the introduction of AECS is very likely to be accepted by ECS users. Hedonic motivation is found to be a much stronger predictor of behavioral intention to use AECS as opposed to safety concern, while performance expectancy and social influence are strong drivers of intention to use ECS and have indirect effects on the intention to use AECS. Multigroup analysis indicates heterogeneous behavioral intention across countries. The multi-faceted empirical results generate insights into the deployment and management of AECS in various contexts

Multi-state supernetworks: recent progress and prospects

Abstract:Supernetworks have long been adopted to address multi-dimensional choice problems, which are thorny to solve for classic singular networks. Originated from combining transport mode and route choice into a multi-modal network, supernetworks have been extended into multi-state networks to include activity-travel scheduling, centered around activity-based models of travel demand. A key feature of the network extensions is that multiple choice facets pertaining to conducting a full activity program can be modeled in a consistent and integrative fashion. Thus, interdependencies and constraints between related choice facets can be readily captured. Given this advantage of integrity, the modeling of supernetwork has become an emerging topic in transportation research. This paper summarizes the recent progress in modeling multi-state supernetworks and discusses future prospects

Integrated modeling of residential and work-related mobilities:A large-scale dynamic Bayesian network approach

Residence and work-related choices are commonly assumed interdependent and associated with some other individuals’ and households’ life events as well as people’s socio-demographics. This paper proposes an integrated dynamic model to capture the determinants of residential and work-related mobilities, taking into account their associations with other life events. The change of employment is modeled with its multi-dimensional features, i.e., change of working hours and change of work place location, while residential mobility is modeled through the change of residential location as well as with whom within the household the move out take place. The Dutch micro-population data from 2015 to 2019 provided by the Netherlands CBS (Census Bureau of Statistics) are used to train and validate a dynamic Bayesian network (DBN) model. The integrated model has a high prediction precision with an average of 89.77% across life events. The results suggest that other life events, such as getting married or divorced, significantly affect the relocation of residence, while changes in working hours strongly influence the work place relocation. However, the relocations of residence and work place in the Netherlands appeared not to be directly intertwined

Effects of life events and attitudes on vehicle transactions: A dynamic Bayesian network approach

Individual and household life events are interdependent and influence mobility-related decisions at different levels over time. This paper developed an integrated dynamic model to capture the interdependences among life events, with a special focus on vehicle transactions. Particular attention was paid to the inclusion of vehicles’ characteristics such as the age, fuel type, and size of cars, which are pertinent to emission forecast. A dynamic Bayesian network (DBN), containing individual and household characteristics and latent attitudes toward car ownership and use alongside life events, was employed to study the interdependences. The temporal relationships among life events and lead-lag effects were also captured in the DBN. The longitudinal survey data “the Netherlands Mobility Panel (MPN)” from 2013 to 2018 was used to train and test the DBN. The analysis results confirm the dynamic interdependences between vehicle transactions and other life events and reveal noticeable associations between attitudes and purchase decisions. It is found that several life events (e.g., “Birth of a baby”, “Marital status change”) have concurrent or varied lag-effects on vehicle transaction decisions. The validation indicates that the proposed DBN approach has a high predictive accuracy of vehicle transaction decisions and other life events

Assessment of the tradeoff between energy efficiency and transfer opportunities in an urban rail transit network

Urban rail transit (URT) in metropolitan areas consumes huge energy. Energy-efficient timetabling (EET) of URT is an essential measurement of URT management and technologies toward carbon neutralization initiatives. However, the majority EET studies focus on single URT lines ignoring passenger transfer and path choice in the entire URT network. As passenger path choice and timetabling are interdependent in a URT network, the ignorance of passenger transfers potentially results in irrelevant energy efficiency of a URT network. This paper proposes a bi-objective EET model incorporating the minimization of passenger transfer times as an objective in addition to energy efficiency. The timetabling objectives and constraints are linearized, and the bi-objective is transformed into a single objective by a linear weighting method. Utilizing the passenger demand and speed profile data of URT in the City of Xi'an (China), a case study is performed to demonstrate the effectiveness of the proposed EET model. The numerical results show that an optimized timetable solution can reduce 25.1% energy consumption and save 3.3% passenger transfer time.</p

Toward energy-efficient urban rail transit with capacity constraints under a public health emergency

Urban rail transit (URT) plays a pivotal role in mitigating urban congestion and emissions, positioning it as a sustainable transportation alternative. Nevertheless, URT’s function in transporting substantial numbers of passengers within confined public spaces renders it vulnerable to the proliferation of infectious diseases during public health crises. This study proposes a decision support model that integrates operational control strategies pertaining to passenger flow and train capacity utilization, with an emphasis on energy efficiency within URT networks during such crises. The model anticipates a URT system where passengers adhere to prescribed routes, adhering to enhanced path flow regulations. Simultaneously, train capacity utilization is intentionally limited to support social distancing measures. The model’s efficacy was assessed using data from the COVID-19 outbreak in Xi’an, China, at the end of 2021. Findings indicate that focused management of passenger flows and specific risk areas is superior in promoting energy efficiency and enhancing passenger convenience, compared to broader management approaches.</p