Travel Characteristics Research Articles

Examining multi-tasking behaviour and ICT utilization during commutes in public transport: the role of personality traits, socio-economic factors, and travel characteristics

ABSTRACT Promoting information and communication technology (ICT) offers several benefits, including an enhanced travel experience, improved security measures, and more effective multi-tasking. However, existing studies on ICT usage, which are predominantly conducted in developed countries, frequently overlook the role of personality traits. Recent research, however, highlights the significant impact of personality traits on travel behaviour. The present study fills this gap by examining the influence of socio-economic factors, travel characteristics, and two key personality traits (conscientiousness and openness) on ICT usage during commuting in Mumbai, India by employing a hybrid choice model. The results unveil negative associations between ICT usage and factors like travelling with companions, lower income, and conscientiousness. In contrast, openness traits, longer travel time, and access to Wi-Fi at home positively impact ICT usage. These findings carry practical implications, offering policymakers insights to craft targeted strategies for promoting ICT usage and leveraging its benefits for urban commuters.

Analyzing Travel and Emission Characteristics of Hazardous Material Transportation Trucks Using BeiDou Satellite Navigation System Data

Road hazardous material transportation plays a critical role in road traffic management. Due to the dangerous nature of the cargo, hazardous material transportation trucks (HMTTs) have different route selection and driving characteristics compared to traditional freight trucks. These differences lead to unique travel and emission patterns, which in turn affect traffic management strategies and emission control measures. However, existing research predominantly focuses on safety aspects related to individual vehicle behavior, with limited exploration of the broader travel and emission characteristics of HMTTs. To bridge this gap, this study develops a comprehensive framework for analyzing the travel patterns and emissions of HMTTs. The methodology begins by applying a Gaussian mixture distribution model to identify vehicle stop points, eliminating biases associated with subjective settings. Origin–destination (OD) pairs are then determined through stop time clustering, followed by the extraction of travel characteristics using non-negative matrix factorization. Emissions are subsequently calculated based on the identified trip data. The relationship between emissions and land use characteristics is further analyzed using geographically weighted regression (GWR). Crucially, this study leverages data from the BeiDou Satellite Navigation System, focusing on HMTTs operating within Shanghai. The processed data reveal three distinct travel modes of HMTTs, categorized by spatiotemporal patterns: Daytime—Surrounding cities, Early morning—In-city, and Midnight—Scattered. Moreover, unlike other road vehicles, HMTT emissions are heavily influenced by industrial and company-related points of interest (POIs). These findings highlight the significant role of BeiDou Satellite Navigation System data in optimizing HMTT management strategies to reduce emissions and improve overall safety.

Optimal scheduling strategy for electric vehicle charging and discharging in different types of buildings based on vehicle-to-building

The application of vehicle-to-building (V2B) technology to integrate photovoltaic charging stations (PVCS) with smart building microgrids has gradually emerged as a new low-carbon operation model in the electric vehicle (EV) energy supply industry. The disordered integration of a large number of EVs into the power grid has caused numerous safety issues for building microgrids. Considering that buildings suitable for the construction of PVCS are primarily concentrated in residential, office, and commercial areas, this study proposes an optimized scheduling strategy for the charging and discharging of electric vehicles that considers different types of buildings. First, a PV-building-EV integrated system based on V2B is established. Probability distribution models of EV travel characteristics for different regions are obtained through actual data fitting, and unordered charging load curves for each region are derived by incorporating the Monte Carlo sampling method. On this basis, considering the interest of the building microgrids, PVCS operators, and EV users, a multi-objective optimization scheduling model for orderly EV charging and discharging based on V2B is established. Finally, the improved Non-dominated Sorting Genetic Algorithm II validates the model's effectiveness.

Factors Affecting Pre-Travel Planning of Families Netnographic Study of Facebook Group “The Travel Diary”

The aim of this research was to identify which factors are taken into account when families – with children or elderly parents – are planning to travel. To understand what these factors are, the Facebook group “The Travel Diary” was observed. For this study, the Theory of Planned Behavior (TPB) was chosen as the theoretical framework. Existing literature has also utilized TPB to study the factors that affect travel. However, the broader themes (under which these factors are categorized) have only been analyzed individually. The focus of this research is to study as many broader themes holistically as possible to gain deeper insights. Additionally, previous research has mainly gathered data through structured interviews. The current research, on the other hand, employed netnography in order to observe online community members to gather authentic, unobtrusive data. The themes that arose from the eight-week observation were traveler characteristics, travel characteristics, destination characteristics, travel arrangements and sources of recommendations. Some factors that, to the best of the researcher’s knowledge, have not been studied yet in literature were food related and baby related arrangements.

Modeling and Analyzing the Spatiotemporal Travel Patterns of Bike Sharing: A Case Study of Citi Bike in New York

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning.

A Spatiotemporal Feature-Based Approach for the Detection of Unlicensed Taxis in Urban Areas.

Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer-Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin-destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures.

A Planning Method for Charging Station Based on Long-Term Charging Load Forecasting of Electric Vehicles

During the planning and construction of electric vehicle charging stations (EVCSs), consideration of the long-term operating revenue loss for investors is often lacking. To address this issue, this study proposes an EVCS planning method that takes into account the potential loss of long-term operating revenues associated with charging facilities. First, the method combines the Bass model with electric vehicle (EV) user travel characteristics to generate a charging load dataset. Then, the cost of profit loss—which represents the EVCS utilization rate—is incorporated into the construction of the objective function. Additionally, a parallel computing method is introduced into the solution algorithm to generate the EVCS planning scheme. Finally, the cost-to-profit ratio of the EVCSs is used as a filtering condition to obtain the optimal EVCS planning scheme. The results show that the EVCS planning scheme considering the profit loss reduces the annual comprehensive cost by 24.25% and 16.93%, and increases the net profit by 22.14% and 24.49%, respectively, when compared to the traditional planning scheme under high and low oil prices. In particular, the charging station strategy proposed in this study has the best effect in the case of high oil prices.

A Transport Mode Detection Framework Based on Mobile Phone Signaling Data Combined with Bus GPS Data

Transport mode is one of the important travel characteristics for citizens, which is crucial to the planning and management of urban transportation. With the natural advantages of large sample sizes and a wide coverage of people, more and more researchers adopt mobile phone signaling data (MSD) to detect transport modes. However, due to their low positioning accuracy and temporally irregular nature, identifying transport modes with similar spatiotemporal features, such as the bus and car modes, is particularly challenging. We propose a transport detection framework using MSD combined with bus GPS data to distinguish between the car and bus modes. First, a trajectory matching algorithm is proposed to obtain the most probable bus that mobile phone users may take. Then, more features are mined to improve the accuracy of transport mode detection with different classification models. Furthermore, for signaling trajectories identified as the bus mode, more bus travel information is recognized, including the boarding and alighting station and timestamp. Finally, we built a ground truth dataset and compared the recognition accuracies under different features and classification models. The result shows that the transport mode detection accuracies of the proposed framework with the GBDT, XGBoost, and LightGBM algorithms are all higher than 94%.

Study on the travelers’ P&R usage behavior during snowfall in the corridor valley pattern city

According to the characteristics of corridor valley pattern cities, this paper presents four aspects of terrain space perception. Using a structural equation model (SEM), the four-dimensional terrain space perception is analyzed through second-order confirmatory factor analysis. A random parameter Logit model (RPLM) was developed to examine the impact of individual socioeconomic attributes, objective built environment, travel characteristics, and terrain space perception on travelers’ Park and Ride (P&R) use behavior during snowfall conditions. Additionally, a Logit model was utilized to assess how individual socioeconomic attributes of travelers in corridor valley pattern cities influence their perception of terrain space during snowfall weather. The findings indicate that the four latent variables related to terrain space perception proposed in this paper effectively explain travelers’ perception of terrain space in corridor valley pattern cities, and the observed variables also support these latent variables. The subjective and objective built environment of corridor valley pattern cities significantly affect travelers’ P&R usage behavior during snowfall weather, and there is heterogeneity in the terrain space perception during snowfall weather among different travelers in corridor valley pattern cities. Furthermore, the gender, age, income, and experience of using P&R of travelers in corridor valley pattern cities play a significant role in moderating terrain space perception.

The role of intermodality and environmental consciousness in the preferences for MaaS bundles: A hybrid choice modeling approach

The interest in Mobility as a Service (MaaS) continues to increase among researchers, where the main driver of successful implementation is the travelers’ adoption of the service, which can be achieved by providing suitable mobility solution in the form of MaaS packages. This paper examines preferences for MaaS packages where a stated choice experiment is conducted among 519 individuals via an online survey in Hungary, and a hybrid choice modeling is estimated allowing the understanding the impact of latent attitudinal variables on preferences. The results show that almost half of the participants would purchase MaaS bundles, but the potential adopters vary according to socio-demographic and travel characteristics. Participants value the inclusion of PT, bike-sharing services, and online shopping voucher discounts in the bundle, while car-sharing and e-scooter-sharing are not so popular. Additionally, transfer between modes and environmental consciousness influence the bundle uptake positively. The findings suggest that future MaaS operators should carefully design mobility bundles to alter travel behavior and obtain a commercially feasible uptake level. The potential negative impacts of MaaS remains a challenge for policymakers and a topic for future studies.

Two-stage distributionally robust optimization operation of virtual power plant considering the virtual energy storage of electric vehicles

Virtual Power Plant (VPP) is a key to aggregate various distributed energy sources. With the vigorous rise of various distributed energy sources, the direct access of large-scale electric vehicle load will increase the complexity of VPP coordinated operation. Hence, this paper proposes a VPP optimization method for Electric Vehicle Virtual Energy Storage (EV-VES). Firstly, the travel characteristics of electric vehicles are analyzed, and EV-VES model is established to coordinate and manage the charge-discharge behavior of EV. Secondly, the “carbon charge rate” model of energy storage (ES) is introduced to establish the relationship between carbon emission and electricity price, and the VPP operation model considering the “carbon charge rate” of energy storage is established. Finally, the two-stage robust optimization operation model of VPP is constructed, Wasserstein distance is used to describe the confidence set of uncertain probability distribution of wind power generation and load, and the uncertainty of system source and load are described by the confidence set. The Column and Constraint Generation (C&CG) algorithm was used to determine the optimal operational benefit solution. The effectiveness of the proposed VPP optimal operation model was validated through case study.

The Impacts of Perceived Safety and Service Quality on Perceived Accessibility by Public Transport in Melbourne

In recent years, accessibility to services and activities using sustainable transport modes has become an important goal for land use and transport planning policies. Traditional approaches rely on objective measures, such as travel time, distance, costs, and other spatial data, overlooking personal preferences, experiences, demographic features, and socio-cultural and economic dynamics. Attributes that affect perceptions of accessibility require attention but our understanding of the factors influencing perceived accessibility is inadequate, particularly in Australia’s expanding suburbs. Through a literature review and questionnaire surveys, this study aims to take into account various travel characteristics and enhance the understanding of how perceived safety and service quality impact mobility behaviour and perceived accessibility within the Australian context. The study found that perceived safety and service quality have significant impacts on perceived accessibility and hence influence the use of public transport. We have identified critical factors that require proper consideration in transport and land use planning and policies to enable a better contribution from public transport to the liveability and well-being of residents in Australian suburbs.

Predicting Electric Vehicle Charging Demand in Residential Areas Using POI Data and Decision‐Making Model

Transport electrification is a crucial element of the ongoing energy transition, essential for achieving carbon peaking and carbon neutrality goals. The proliferation of electric vehicles (EVs) introduces significant challenges to power distribution network stability due to their aggregated charging load in residential areas, particularly during peak electricity consumption periods. This paper proposes a method to predict the spatiotemporal distribution of EV charging demand in residential areas using geographic information points of interest (POI) data features and a decision‐making model. Utilizing real historical data, probability distribution models for EV users' arrival times and charging characteristics were constructed using Gaussian Mixture Models (GMM). The spatiotemporal characteristics of EV travel and charging behaviors were analyzed, and a comprehensive charging decision model incorporating both emergency and stochastic scenarios was developed. The model's efficacy in capturing the probability distributions of characteristic variables was validated through a case study. The results demonstrate the model's potential for accurately predicting EV charging demand, providing valuable insights for infrastructure planning and resource allocation. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Residential and experienced social segregation: the roles of different transport modes, metro extensions, and longitudinal changes in Hong Kong

Social segregation is a challenge intensified by rapid urbanisation. Using mobility data, researchers have widened the scope of analysis from static residential segregation patterns to experienced segregation in activity space. Despite such progress, we have yet to decipher how social segregation evolves with the urban development process. The different roles of transport modes, major transport infrastructure expansion, and longitudinal changes over decades are rarely captured. This study utilises three Hong Kong Travel Characteristics Surveys data from 1992 to 2011 to analyse the city’s social mixing changes. Detailed mobility and socio-demographic data of 101,385 (2011), 92,520 (2002) and 77,271 (1992) individuals were included. We found that the unequal experience of social segregation among different social demographic groups has persisted across the two decades. Nevertheless, public transit has a significant effect in increasing social mixing, thereby moderating the experienced social segregation. The roles of buses and the metro system are particularly noteworthy. Nonetheless, we found a selection bias of new metro stations in areas of high social mixing already. Over time, the metro expansion plays a paradoxical role. On one hand, the metro stations’ immediate catchment areas have seen a decreased low-income population. On the other hand, the increased number of metro trips allows people to have a more diverse experience.

School travel behaviors: How the pandemic impacted communities

The global pandemic in early 2020 significantly disrupted life for many families, and the trip to and from school was not immune to these disruptions. Parents and children alike made travel adjustments depending on their preferences with regard to personal health and safety, social distancing, and aversion to risk. Each school district and individual school also made decisions with regard to in-person or remote learning during this period of uncertainty.In this study, a research gap was filled by examining the effect of the pandemic on school transportation-related decisions for hundreds of families living in the United States. An online survey was developed and administered with the help of Qualtrics, an experience management company. Over 600 responses were gathered from across three Pacific Northwest states. In addition to collecting demographic data about the respondents, the survey asked about travel mode choices and characteristics of the trip to and from school. The collective results were then analyzed to determine which factors directly contributed to pandemic-related changes in travel behavior.The study concluded that parent education level, household income, and age of child were all statistically significant variables that affected behavioral change. Additionally, common travel assumptions associated with rural students, when compared with urban students, were confirmed. These factors included a greater reliance on a yellow school bus and lesser availability of critical infrastructure.

Sustainable Steps in the Snow: Exploring factors associated with active school commuting in Finland

Physical inactivity of children and youth is a major health problem. Active commuting could increase the amount of physical activity and be a more environmentally friendly traffic mode choice compared with motorized vehicles. Parents have an integral role in determining the options a child has for commuting. We explored parental barriers and motivators associated with the active commute of their children.This cross-sectional study used the CLIMATE NUDGE Survey data of parents with school-aged children. A set of 25 possible influencing factors included demographic factors, social aspects, and pro-environmental attitudes, and questions about the motivating role of journey characteristics and health effects. The data were analyzed with two multinomial logistic regression analyses (N = 320): first, comparing those who either never or sometimes commute actively to those who always do so, and second, those who always or sometimes commute actively to those who never do so.Results indicated the strongest determinants to differentiate those who always and those who never use active commute, were commute distance and parental beliefs about social commute norms. The longer the commute distance was, the more likely the child was to belong to the never active commuter group and less likely to belong to the always active commuter group. The more parents believed their child’s peers commuted actively, the more likely it was that their child always commuted actively and less likely they never did so.We found several factors associated with active commuting, yet somewhat surprisingly neither environmental attitudes nor climate change related reasons were significantly associated with active commuting. To conclude, instead of highlighting environmental aspects to promote active commute, we recommend highlighting social norms and perception of the commute distance, and addressing weather-related barriers and health related motivators.

Block-level Trip Generation/Attraction and Travel Characteristics Using GPS Data and Building Usage Composition in Tokyo Wards

Block-level Trip Generation/Attraction and Travel Characteristics Using GPS Data and Building Usage Composition in Tokyo Wards

Investigating work commuters mode choice behavior in Dalian city during pandemic: a hybrid choice model approach

ABSTRACT This study examined commuters' mode choice behavior during the COVID-19 pandemic, focusing on the impact of both tangibles and intangibles factors. To do so, the authors developed a Hybrid Choice Model (HCM) which investigated the effects of sociodemographic characteristics, travel attributes, and intangible factors such as satisfaction, safety, and discomfort, on travel behavior. The model was tested using Stated Preference data collected from commuters in Dalian City (China) via wenjuanxing, an online survey platform. The results indicate that individual characteristics played a significant role in shaping people's perceptions and mode choice behavior was influenced by sociodemographic factors, travel characteristics, and the aforementioned intangible factors. More specifically, individuals, who perceived a higher level of safety, measured in terms of worries about robbery, accidents, and pandemic concerns, were less likely to choose cars. People with a stronger perception of discomfort favored metro over bus. Private car users reported higher satisfaction. The findings will be useful to policymakers for establishing more resilient transport solutions.

EV charging load forecasting and optimal scheduling based on travel characteristics

Accurate prediction of EV charging load distribution is not only the basis of evaluating the capacity of distribution network to receive EV, but also the necessary premise to promote the research of charging station planning and vehicle-network interaction. Based on this, firstly, according to the travel characteristics of electric private car and taxi users, this paper constructs the models of speed-flow and EV power consumption per mileage, thus, the real-time changes of vehicle speed and power consumption per mileage are simulated. Secondly, consider factors such as charging price, time, and power consumption along the way, as well as multiple sources of information such as traffic networks, vehicles, public quick charging stations, and distribution networks, the OD matrix analysis method is introduced to simulate EV moving characteristics for EV trip assignment matrix, and the EV charging load forecasting system with real-time interaction of multi-source information is established. Finally, the second-order cone optimization method is used to optimize the space-time distribution of regional charging load. The model and method proposed in this paper are more consistent with the actual situation and can improve the accuracy of prediction.

How do Road Users in Mixed Traffic Perceive Risk Towards Each Other? A Comparative Study from a Developing Country

Many developing countries have high traffic accident rates due to the mixed traffic environment in which bicycles and motor vehicles travel in the same lanes. This paper examines the perceived risk among road users in mixed traffic. A questionnaire survey was used to obtain demographic information and self-reported risk perception among cyclists, motorcyclists, and drivers. Statistical analyses, including the T-test and ANOVA, were used to examine the relevant differences in risk assessment among the groups. The results demonstrate that the groups differ significantly in sociodemographic characteristics, driving experience, and travel characteristics. For self-reported perceived risk, the respondents’ perceptions of road surface conditions, the potential of daydreaming while operating a vehicle, and distraction from passengers or riding companions exhibited significant differences between the groups. The research findings could be beneficial as a guide for policymakers in planning road safety awareness to reduce risk in mixed traffic environments.