Daily Travel Research Articles

Model Checking Spatial Reachability Specifications of Public Transport Networks

Well-designed spatial configurations of public transport stops and routes in big cities contribute to enhancing daily travel services for citizens, effectively mitigating traffic congestion, and addressing other pertinent challenges. When examining spatial layouts of public transport networks (PTNs), various reachability demands between stops or urban Points of Interest (POIs) are crucial issues should be firstly taken into account. Existing methods to investigate spatial reachability properties of PTNs generally need to construct some evaluation functions, or survey reachability metrics through some network analysis techniques. These methods are often impractical, as the functional relations always cannot be accurately defined, or some global network metrics cannot provide explicit evidences for PTN layout planning or optimization.In this paper, we introduce spatial model checking techniques to the formal verification of the reachability specifications of PTN to guarantee the rationality of PTN layout. First, we extend closure space structure by incorporating attribute labeling functions and logical propositions for public transport stops and routes to develop a formal spatial verification model for PTN spatial layout. Second, we propose several novel reachability operators based on the logical operators of the Spatial Logic for Closure Space (SLCS) to facilitate the logical characterization of reachability specifications. Third, we perform the verification of the transformed reachability formulas by the spatial model checker topochecker. Examples demonstrate the effectiveness of our approach and indicate that it can perform automatic, descriptive and comprehensible verification of the reachability properties PTN layouts.

Exploring disparities and similarities in daily travel mode choices between electric vehicle owners and internal combustion engine vehicle owners

ABSTRACT This study proposes a novel instrument for measuring private car owners’ travel-related lifestyle (PCTRL) based on a survey (N = 2045) conducted in China’s four first-tier cities and ten quasi first-tier cities. By employing principal component analysis (PCA) and cluster analysis, the study classifies electric vhehicle (EV) owners and internal combustion engine vehicle (ICEV) owners into five distinct PCTRL segments, respectively. The findings reveal that only 66% of EV owners have similar PCTRLs to 67% of ICEV owners, with the most notable disparity being the higher likelihood of EV owners choosing private cars for their daily commutes. Furthermore, a random forest model was developed to predict the daily activity-based travel mode choices behavior of both EV and ICEV owners. The findings offer valuable insights into understanding the behavior of EV and ICEV owners in their daily travel mode choices and help formulating more effective traffic management strategies.

Intelligent Assisted Travel Wheelchair Based on Image Recognition Technology

This paper introduces an intelligent image recognition system integrated into a wheelchair based on deep learning in cold environments, aiming to improve the convenience and safety of disabled individuals. The system adopts advanced image recognition technology to monitor road conditions in real-time through the camera and to detect and measure distance to foreign objects on the road. The system visualizes the detection results on the wheelchair screen to assist the user in avoiding and improving the safety of their daily travel. In addition, the system also includes crawler tracks, seat heating, snow and rain protection, and other functions. The wheelchair has a wide range of application prospects and development potential. It is expected to be widely used in the future, providing a strong guarantee for the safe travel of disabled individuals in China.

Optimizing customized bus services for multi‐trip urban passengers: A bi‐objective approach

AbstractCustomized bus services typically focus on single‐trip requests, which often struggle to accommodate the growing needs for varied multiple trips in urban daily travel. This paper addresses the customized bus routing problem for passengers with multiple trips. A bi‐objective mathematical model is established for maximizing the operational profit and minimizing the travel costs by considering the characteristics of the multi‐trip requests and time‐dependent travel time. Besides, a novel profit objective function is proposed considering the service's completion status and the starting price. Since the proposed mixed integer linear programming model is an NP‐hard problem, a non‐dominated sorting genetic algorithm II‐based method is proposed to handle different sizes of instances. Finally, the instances with multi‐trip requests are carried out to test the accuracy of the model and the effectiveness of our method compared with Gurobi and the local search‐based multi‐objective algorithm approach.

A dynamic discrete choice modelling approach for forward-looking travel mode choices

In this paper, we present a systematic approach based on dynamic discrete choice models (DDCM) to investigate individuals’ forward-looking mode choice behaviours in daily travel tours with multiple destinations. We propose a novel network transformation model that encompasses the entire decision space of all feasible mode combinations for every observed trip/tour in the dataset. By applying the well-established Recursive Logit model structure commonly used in path choice modelling, we address the tour mode choice problem effectively and quantify forward looking considerations in the mode choice process. The proposed model captures the complex considerations individuals take into account when making mode choices. The network transformation incorporates downstream mode limitations into the preceding mode choice options, enabling us to model individuals’ forward-looking behaviour and gain insights into how considerations of future trips such as a shopping in the evening, or school pick-up trip influence previous mode choice decisions earlier in the day. Uncovering and quantifying these hidden forward-looking factors can help modellers better explain the private car usage usually observed for the entire sequences of daily trips, even in presence of competitive alternative modes. The proposed network transformation also enables us to measure the effect of the requirement/preference to return private vehicles (car, motorcycle, and bicycle) home on mode choices in home-bound trips, and subsequently, on the entire daily mode choice decisions. To validate the proposed model, we utilise the VISTA household travel survey data from the Melbourne Metropolitan area in Australia. The model is compared against baseline models, demonstrating its statistical advantages. Additionally, intuitive illustrations using the data showcase the model’s efficacy. From transport planning and policy perspective, tour-based mode choice modelling provides a more comprehensive and precise understanding of travel behaviour by considering the sequence of trips made by an individual. This can help capture the interactions and dependencies between different trips, which trip-based models may overlook. The proposed model is more suitable for analysing the effects of policy interventions like congestion pricing, public transport investments, or new mobility initiatives, as they can better represent the cascading effects of such policies across multiple trips.

Impact of COVID-19 led transition of work culture and travel to work patterns on society and environment in Delhi

Globally, COVID-19 has led to changes in daily lifestyle and travel behavior. There was a shift from in-person physical jobs to work-from-home culture, even for those jobs that required in-person presence at the job location. However, this was not true for everyone, and a majority continued to travel to work. The limited capacity of public transport services and the fear of exposure to the virus restrained people from practicing their conventional mobility choices. We explore the impact of COVID-19 on the nature of jobs and related changes in work travel patterns by population groups for Delhi using in-person surveys conducted during 2021 in six localities of Delhi. We also estimated the short- and long-term impacts of altered behavior on expenditures and equivalent CO2 emissions.As per the analysis, lower income groups, having no vehicles, with lesser qualifications, and employed as daily wagers, had limited capacity to change to work-from-home during COVID-19. We observed an increased dependency on personal motorized vehicles, leading to increased expenditures and reduced affordability. Adopting work-from-home during COVID-19 helped reduce equivalent CO2 emissions per working member by 39%. However, as the work at the site resumes, the emissions are expected to increase. The study shows that COVID-19 led to short-term benefits, but in the long term, the externalities will likely increase. Working from home during the pandemic provides short-term benefits; the social benefits are not equally distributed, and the transport emissions shall increase in the long term.

Assessment of the Transportation and Mobility Impacts of Santa Rosa Bridge Rehabilitation on Student Commuters

This study assessed the transportation and mobility impacts of the Santa Rosa Bridge rehabilitation on student commuters, focusing on how this infrastructure project affects their daily travel experiences. The study employs a descriptive-comparative methodology, gathering data from 117 student commuters through a researcher-made questionnaire distributed online. Results reveal a significant increase in travel time, with more students experiencing delays of 1-2 hours or more. Additionally, the rehabilitation has led to decreased satisfaction with available transportation options and overall commuting experiences, as evidenced by a decline in weighted mean scores across all transportation and mobility constructs. The study concludes that the rehabilitation project has adversely affected student commuters, disrupting their routines and reducing the reliability of their commutes. These findings highlights the need for proactive measures, such as alternative transportation options or temporary shuttle services, to mitigate the impact on student mobility during infrastructure projects.

The barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC) adoption

The rise in motorcycle use in Southeast Asian countries like Indonesia has caused environmental issues and transitioning from fossil-fuel to electric motorcycles (EM) will reduce emissions and improve air quality. This study aims to investigate the barriers, determinants, and willingness-to-pay in electric motorcycle conversion (EMC). In a choice experiment, data from motorcycle users in Bali, Indonesia, was collected and analysed in this study using K-modes cluster analysis and the Mixed-Logit Model. The study identified different barriers to EMC among motorcyclist groups: mix-motor commuters and hardcore oldies concerned with financial challenges, all-day riders with mature motorcycles face a lack of information on EMC costs and procedures, and higher-power enthusiasts and newbies with light motorcycles concerned with daily travel disruptions during the conversion process. This study also found waiting and conversion time to play a role in EM adoption and travellers who use older motorcycles are the most likely to adopt EMC. Lower-income individuals tend to be more inclined towards EMC and younger demographics lean towards internal combustion engine (ICE) motorcycles. Moreover, the study indicates that EMC reduces the adoption of ICE motorcycles more than conventional EM. A 50% increase in conversion time lowers EMC adoption probability by 5.2%pts. and increases new ICE motorcycle adoption by 3%pts. and new EM adoption by 2.2%pts. Additionally, motorcyclists are more willing to invest in EMC if it means reducing conversion time/charging costs, particularly for older motorcycles. This study offers several policy recommendations for accelerating EMC adoption in Indonesia.

A framework for modal split and implications on transport growth and travel time savings

Mobility is one of the most debated issues in the current discussion on transport policies and the climate change. Related indicators are the modal split, transport growth and travel time savings, used for travel behaviour evaluations. In official transport statistics, the modal split is generally used in two ways, according to the number of modal trips and to the modal trip length. Although they are usually used separately, we will show that they are directly connected, where the latter is a weighted version of the former. The introduction of the reference units of “person” and “day” overcomes the limitations of a trip-based definition and extends the ansatz to a mobility-based methodology, where all modal combinations can be considered. Empirical data analysis shows, that transport growth is founded on the increase of individual motorised transport at the disadvantage of pedestrians, whereas other modes remain fairly stable. Daily travel time generally increases especially with individual motorised transport in comparison to non-motorised modes, which shows an inconsistency, for example, for some assumptions or approaches of travel time savings. The proposed methodology does not require any prior assumptions and provides therefore a simple and general applicability. The data analysis is based on national travel survey data from Germany, Switzerland, UK and USA from the 1970-ies up to 2018. One main result of the paper is a frame of reference for the relativity of mobility behaviour, which consistently connects these mobility indicators.

Measuring the effectiveness of incorporating mobile charging services into urban electric vehicle charging network: An agent-based approach

Mobile charging service (MCS) has emerged as a cost-efficient solution to compensate for the limitations of fixed charging stations (FCSs) in service capacity and coverage. Nevertheless, the impacts of incorporating the MCS into the charging infrastructure network have not been well understood. Therefore, this study introduces an exhaustive framework for devising Agent-based Modeling (ABM) to delineate electric vehicle (EV) drivers' daily travels and charging behaviors within the integrated charging network. Specifically, the developed ABM framework encompasses both internal attributes of individuals and external variables of charging and transport networks that potentially shape EV drivers’ public charging decisions. It generates authentic daily charging demands for FCSs and MCSs and provides a streamlined process to further incorporate more charging modes into the EV charging ecosystem. Subsequently, we opted for the Salt Lake City metropolitan area to implement our framework and showcase the effectiveness of MCS integration on the charging network over various EV adoption levels and network settings. The findings of the designed case study unravel the choice patterns of EV drivers when facing diverse charging options and pinpoint the strengths and weaknesses of MCS incorporating into the urban charging network at different stages.

‘Green or short: choose one’ - A comparison of walking accessibility and greenery in 43 European cities

Promoting environmentally and socially sustainable urban mobility is crucial for cities, with urban greening emerging as a key strategy. Contact with nature during travel not only enhances well-being but also promotes sustainable behaviour. However, the availability of travel greenery varies, and only recently have new datasets and computational approaches made it possible to compare the conditions in the distribution of travel greenery within and between cities quantitatively. In this study of 43 large European cities, we undertook a comparative analysis of travel greenery availability by using high-resolution spatial data and daily school trips as a marker of a daily travel need. By recognising walking accessibility as the most sustainable and equally available mode of transportation, we first estimated the proportion of the population residing within walking distance to upper secondary schools. Second, we associated the detailed school routes with monthly green cover data and compared the spatial variation in travel greenery availability between European cities, taking seasonal variation into account. Lastly, we analysed spatial inequalities of travel greenery availability within the study cities using the Gini index, the Kolm-Pollak equally-distributed equivalent (EDE) index and Moran's I. Our findings reveal a consistent negative association between accessibility and green cover implying a trade-off between access and greenery. We found large variations between European cities in the walking accessibility of schools, ranging from 44% to 98% of the population being within 1600 m of their school. Moreover, our results show substantial within-city disparities in travel greenery availability in large European cities. We demonstrated methodologically the importance of considering seasonal variations when measuring greenery availability. Our study offers empirical evidence of urban greenery availability from a mobility-focused perspective. It provides a novel understanding with which to support researchers and planners in affording the benefits of nature to more people as they travel.

Factors driving reduction in CO2 emissions from personal travel: A repeated cross-sectional analysis

This study identifies factors that drive the changes in CO2 emissions from personal travel in urban areas during 2006 and 2016 using repeated cross-sectional household travel surveys conducted in Seoul, Korea. We first estimated the daily travel CO2 emissions of every survey participant for each year based on travel itinerary records and estimated CO2 intensity for the travel mode. The results suggest that total CO2 emissions from personal travel in Seoul declined remarkably between 2006 and 2016, potentially due to reduced vehicle use, transition to non-motorized travel, improved vehicle technology, and eased traffic conditions. Two multilevel mixed-effect Tobit regression models were developed to identify the factors affecting CO2 emissions from personal travel for each year. The results suggest that some cohorts (e.g. males, 30s and 40s, small households, high-income groups, car owners) are likely to produce more CO2 emissions from personal travel consistently in 2006 and 2016. The changes in the estimated coefficients between the two years were also evaluated statistically, suggesting that some population groups (e.g. elderly, large households) are likely to reduce CO2 emissions from personal travel. The reduction in CO2 emissions by these groups may be correlated with continued investment in the public transportation system in Seoul. These findings provide an opportunity to gain a clear understanding of travel behavioral changes related to CO2 emissions from personal travel, with insights toward sustainable development.

A stochastic ridesharing user equilibrium model with origin-destination-based ride-matching strategy

Ridesharing, as an emerging mode of modern urban transportation, has gained widespread popularity due to its ability to improve convenience and reduce the expenses in daily travel. To capture its impact on the network-wide traffic flow pattern, this paper develops a new variational inequality (VI) formulation for modeling the stochastic ridesharing user equilibrium (SRUE) problem considering an origin–destination (O-D) -based ride-matching strategy. The proposed SRUE problem is formulated based on a hierarchical logit choice model to depict the multi-stage decision-making (i.e., role choice, ride-matching, and route choice) of the ridesharing travelers. The SRUE model also reflects a two-sided market equilibrium considering the interaction effect between the ridesharing market and the road traffic equilibrium market. The logit choice model captures the ride-matching problem between the drivers and the passengers traveling on the same/different O-D pairs. Moreover, the lower- and upper-bound constraints for a ridesharing driver picking up passengers are established at the O-D level. For solving the SRUE model with O-D-based ride-matching constraints, the Lagrangian dual method (LDM) is adopted by incorporating the Barzilai-Borwein (BB) step size to update the dual variables, which accelerates the convergence of the LDM. Three numerical examples are conducted to illustrate the features of the proposed SRUE model and demonstrate the performance of the solution algorithm.

Acceptance and demand of autonomous vehicles for long-distance recreational travel: An investigation based on a survey of visitors to US national parks

The study of anticipated changes in travel behavior that could be brought by autonomous vehicles (AVs) has been widespread for commuting and short-distance daily travel, yet little attention has been given to long-distance travel. To address this research gap, this paper presents an analytical model for estimating public preference towards the adoption and use (in terms of frequency and length of trips) of AVs, focusing on long-distance recreational travel (LDRT). The model considers socio-demographics, trip-specific characteristics, in-vehicle time use-related factors, and attitudinal latent variables (AV usefulness, AV concern, driving enjoyment) as potential influencers of AV acceptance and demand. To analyze the proposed model, data were collected from a survey of visitors to US national parks conducted in Summer 2022, and the structural equation modeling (SEM) technique was employed. The results indicate that the frequency and length of long-distance recreational trips will likely be higher in the AV era. This draws attention to tourism destination managers not only to manage the tourists’ demand at destinations but also to manage traffic on the roads leading to the destinations. Additionally, the results show that the LDRT demand will continue to rise with the increase in AV acceptance due to the usefulness and potential for several in-vehicle activities offered by vehicle automation, despite concerns about system safety, data privacy, and legal liability. The study also reveals that some travelers will likely miss manual driving enjoyment in AV driving, particularly in unique travel settings like tourism travel, and thus might opt for manual driving options. Based on these findings, we advocate for the timely consideration of induced LDRT, and tourism travel demand generated by AVs—such as provisioning sustainable public transport options to recreational and tourism destinations.

Who uses night trains and why? A mixed-method study profiling night train users in Switzerland

Addressing air travel demand is a key open challenge in transitioning to a low-carbon society. In Switzerland, where most flights are from/to nearby European countries, their substitution with night train services is a promising alternative. However, still little research investigates whether the demand for night trains is well-established and the motivations behind it. We aim to bridge this gap through a mixed-method study involving 389 Swiss residents who travelled by night train in 2022/23. A web survey was first conducted to identify latent classes of participants based on their daily and long-distance travel mode choices and evaluate relevant predictors of class membership. Participants of each class were then randomly selected to be involved in an in-depth interview on personal experiences and motivations underlying their choices. Results identify three classes of travellers distinguished by a predominant use of rail and public transport (greens), mixed long-distance travel mode choices and car-oriented daily mobility (pragmatists), preference for flying combined with public transport and active daily mobility (dissonants). Travel cost reduction is the only motivation significantly differentiating pragmatists and dissonants from the greens. This result suggests that fluctuations in ticket prices are the main driving force in shifting demand from flights to night trains.

Fare-free, not carefree: care mobilities in a fare-free public transport system in Tallinn

The implementation of fare-free public transport (FFPT) in Tallinn (Estonia) in 2013 sparked international media, policy, and academic interest in best practices, funding structures, and ridership. Initial studies showed marginal effects on modal shift, but increased travel by low-income households, and by younger and older passengers. Yet, the assumed social impact of FFPT has since been under-researched. Based on qualitative research with 22 transport-dependent users, including two semi-structured interviews and a seven-day travel diary, this study examines the daily experiences, mobility constraints, and travel practices of care mobilities, i.e., journeys made to care for others or a household. Findings indicate that the absence of fares, although a relative variable in modal choice, allows carers expanded activity spaces, independence from car ownership, and easier coordination of care tasks. I propose to frame accessibility as a relational process emerging from passengers’ encounters and the practices adopted to navigate shared spaces. With this, I argue that understanding public transport use and experiences at a micro-level offers an intersectional and justice lens to commuter-oriented transport and neoliberal urban planning policies.

Research on Probabilistic Load Modeling and Simulation Model of Electric Vehicle Charging Station

Abstract The stochastic nature of electric vehicle charging station loads poses certain challenges in establishing relevant load models. This paper introduces a probabilistic load modeling approach that considers factors such as the time electric vehicles begin charging, daily travel distance, charging power, and charging duration. By comprehensively studying the influencing factors of charging station load modeling and combining Monte Carlo simulation with probabilistic statistical analysis, a comprehensive probability load model for typical charging stations is developed. Building upon this, a charging load simulation model is constructed using Simulink. This model can generate daily load curves consistent with the probability load model, making a positive contribution to the study of the impact of electric vehicles on the power grid.

Exploring the perception of quality of life in urban daily commuting for sustainable urban transport in Bangkok, Thailand

AbstractQuality of life (QoL) in daily travel is increasing in popularity as a research topic since transportation infrastructures and services are instrumental in accessing basic services and social capital benefits in areas such as public health, employment, housing, etc. This accessibility has consequently led to improved QoL for the Bangkok population. In this study, the evaluation of the perception of QoL during the daily travel of Bangkokians in Sukhumvit District, Thailand is conducted using face-to-face interview questionnaires with 500 respondents. The structural equation model (SEM) is employed to quantify QoL and its related multidimensional determinants. Four statistically significant factors affect QoL from the travel perspective: (1) accessibility (p-value 0.001), (2) travel cost (p-value 0.05), (3) environment (p-value 0.05), and (4) information (p-value 0.05). Interestingly, accessibility was found to have the most influence on QoL in daily travel. Therefore, policymakers are recommended to consider the degree to which QoL may be affected to establish transportation policies that are more acceptable, practical, and efficient.

Nationwide synthetic human mobility dataset construction from limited travel surveys and open data

AbstractIn recent years, the explosion of extensive geolocated datasets related to human mobility has presented an opportunity to unravel the mechanism behind daily mobility patterns on an individual and population level; this analysis is essential for solving social matters, such as traffic forecasting, disease spreading, urban planning, and pollution. However, the release of such data is limited owing to the privacy concerns of users from whom data were collected. To overcome this challenge, an innovative approach has been introduced for generating synthetic human mobility, termed as the “Pseudo‐PFLOW” dataset. Our approach leverages open statistical data and a limited travel survey to create a comprehensive synthetic representation of human mobility. The Pseudo‐PFLOW generator comprises three agent models that follow seven fundamental daily activities and captures the spatiotemporal pattern in daily travel behaviors of individuals. The Pseudo‐PFLOW dataset covers the entire population in Japan, approximately 130 million people across 47 prefectures, and has been compared with the existing ground truth dataset. Our generated dataset successfully reconstructs key statistical properties, including hourly population distribution, trip volume, and trip coverage, with coefficient of determination values ranging from 0.5 to 0.98. This innovative approach enables researchers and policymakers to access valuable mobility data while addressing privacy concerns, offering new opportunities for informed decision‐making and analysis.

Non-linear effects of children's daily travel distance on their travel mode choice considering different destinations

The scientific layout of child-friendly facilities in the community is of great significance to children's daily activities and healthy growth, which is also a vital concern of the United Nations. However, there is little evidence to prove the intrinsic mechanism of children's daily travel behavior and daily travel destinations. Therefore, this study takes 118 communities in Shuangliu District, Chengdu, as an example and uses a eXtreme Gradient Boosting (XGBoost) model to explore the non-linear effects and threshold effects of children's daily travel distance on their travel mode choices by considering different destinations of children's daily travel in the communities. We found that children's daily travel distances and destinations are vital for their travel mode choice, especially the most significant effect of travel distance. For different children's daily travel destinations, travel distance has a more substantial threshold effect on children's non-walking travel mode choice. Therefore, to scientifically and rationally distribute child-friendly facilities in the community, which may help children's healthy development, a child-friendly city should be planned, constructed, or renewed through the internal logical relationship between children's daily travel destinations, travel distances, and travel mode choices should be considered comprehensively.