Daily Travel Research Articles

Discover Trip Purposes From Cellular Network Data With Topic Modeling

The widespread use of mobile phones has generated a large amount of individual trajectory data. Such data can greatly help analyze and understand human daily travel behavior. In this paper, we use the topic modeling technique to infer trip purposes based on pseudonymized users’ trajectory data from cellular network and points of interest (POIs) from online map services. The adapted latent Dirichlet allocation method is used to model the trip generation process and then infer trip purposes behind the data. The experiments are performed on a data set of 27,732 trip records in Beijing on weekdays. Ten topics are discovered. This method can easily infer different trip purposes based on three trip attributes, i.e., trip departure time, stay duration, and POI categories for destinations, and most of the topics/trip purposes are explainable.

Charging infrastructure planning for ride-sourcing electric vehicles considering drivers’ value of time

ABSTRACT A reliable-charging network is urgently demanded to support electrified ride-sourcing services due to their shorter dwell time, longer daily vehicle miles traveled, and concerns of sacrificing revenue for charging activities. We developed an integer programming (IP) model for the optimal allocation of charging stations and charging plugs to minimize the total investment costs and spatio-temporal varying drivers’ value of time (VOT) for charging activities. The trip chain data of the RideAustin ride-sourcing services have been used as a test case, based on which we estimated the charging needs of ride-sourcing EVs and identified candidate charging locations to fulfill the daily travel needs of ride-sourcing drivers. Through numerical study and sensitivity analyses, we analyze the impacts of different charger types, fleet sizes, government incentives, and VOT considerations on the optimal investment plans and system costs, and show the importance of considering ride-sourcing drivers’ VOT into charging infrastructure planning.

Impact of COVID-19 on daily travel behaviour: a literature review

AbstractThe coronavirus disease 2019 (COVID-19) pandemic made a perceptible impact on daily travel behaviour worldwide, especially through mode shifts and changes in trip frequencies with possible long-term repercussions. Non-therapeutic interventions adopted worldwide (e.g. lockdowns and travel restrictions) to reduce viral contagion need to be understood holistically because it is challenging for people to follow through these policies and stay home in developing nations. In this context, it is important to have a clear idea of how COVID-19 is shaping the mobility pattern and what policies must be taken (if not yet) to minimize viral transmission as well as develop a sustainable transportation system. To this end, this study presents a systematically analysed review of 56 international literatures from academic sources (Google Scholar, Scopus and Web of Science) on the impacts of COVID-19 on travel behaviour and focuses on policymaking measures. This article illustrates the modal shift, variation in frequencies of different trips and how sociodemographic characteristics have influenced the mobility pattern in response to COVID-19. Innate changes in travel patterns compared to the pre-COVID-19 era were observed. A noticeable apprehension on viral transmission in public transit has reduced public transit usage while increasing that of private vehicles. This poses challenges to develop sustainable transportation. This study concludes by discussing intervention measures to support transportation planners and policymakers to deal with the current pandemic as well as any future pandemics.

The MOBIS dataset: a large GPS dataset of mobility behaviour in Switzerland

This article presents the MOBIS dataset and underlying survey methods used in its collection. The MOBIS study was a nation-wide randomised controlled trial (RCT) of transport pricing in Switzerland, utilising a combination of postal recruitment, online surveys, and GPS tracking. 21,571 persons completed the first online survey, and 3680 persons completed 8 weeks of GPS tracking. Many continued tracking for over a year after the study was completed. In the field experiment, participants participated through the use of a GPS tracking app, Catch-my-Day, which logged their daily travel on different transport modes and imputed the trip segments and modes. The experiment lasted 8 weeks, bookended by two online surveys. After the first 4-week control phase, participants were split into two different treatment groups and a continued control group. An analysis of the survey participation shows that the technology is capable of supporting such an experiment on both Android and iOS, the two main mobile platforms. Significant differences in the engagement and attrition were observed between iOS and Android participants over the 8-week period. Finally, the attrition rate did not vary between treatment groups. This paper also reports on the wealth of data that are being made available for further research, which includes over 3 million trip stages and activities, labelled with transport mode and purpose respectively.

Characterizing mobility patterns of private electric vehicle users with trajectory data

Human mobility pattern analysis has received rising attention. However, little is known about the mobility patterns of private Electric Vehicle (EV) users. In response, this paper characterized mobility patterns of private EV users using a unique one-month dataset containing moving trajectories of 76,774 actual private EVs in January 2018 in Beijing. Specifically, we first explored the diversity, regularity, spatial extent, and uniqueness of EV users’ mobility patterns. The results suggested that most EV users had both regular travel and activity patterns (the mean travel and activity entropies were 2.17 and 1.83, respectively) with special preferences towards some specific activity locations relative to all the locations they visited (the mean number of activity locations visited was 13.57 in one month). Furthermore, they tended to perform activities within a small geographical area (the mean radius of gyration was 7.60 km) and have a short daily travel distance (the mean value was 37.35 km) relative to their electric driving range. Further, we associated EV users’ mobility patterns with the built environment through ordinary least squares and geographically weighted regression models, particularly considering the so-called modifiable areal unit problem (MAUP). Due to the MAUP, most of the statistically significant built environment variables varied across spatial analysis units (SAUs). Gymnasia was the only variable statistically associated with the mobility patterns for all SAUs; while the variables related to residence and workplace were not statistically associated.

A satellite perspective on the movement decisions of African elephants in relation to nomadic pastoralists

AbstractThe African savannah ecosystem is populated by nomadic pastoralists who herd livestock in the day and corral them at night in temporary enclosures, called bomas, to protect them. The number and distribution of bomas on the savannah is important from an ecological perspective and may have a significant impact on wildlife movement. However, no study has yet examined this relationship. Here, using very high‐resolution satellite imagery from two time periods, we quanitified changes in boma distribution and density across an area of 3377 km2 in the Laikipia‐Samburu ecosystem of northern Kenya between 2011 and 2019. To assess wildlife movement in relation to bomas, we used a GPS data set on African bush elephant Loxodonta africana movement from 27 collared matriarchs representing herds of 9–15, covering 112 467 hourly GPS fixes over 31 months between 2018 and 2020. Our results showed a more than 46% increase in the total number of human‐built structures between 2011 and 2019, the majority of which were bomas, representing a 21.9% increase in human‐modified land area. Elephants readily adjusted their foraging habits and itineraries in this habitat shared with humans, who were also nomadic in space and time. Assessing the night–day activity ratio, we found elephants move more nocturnally when in closer proximity to bomas, particularly during the dry season. This temporal separation means elephants avoid the times humans are active in and around bomas while still accessing required resources—water and forage. The temporal shift was stronger during the dry season when shared resources are scarce. Using daily travel distance as a metric, we show elephants moved further in closer proximity to bomas which was likely linked to the need to travel between forage patches. Given the rise in human settlements, understanding the consequences of animals' behavioral adjustments is critical to understand the long‐term population viability of elephant populations.

Missed connections? Everyday mobility experiences and the sociability of public transport in Amsterdam during COVID-19

ABSTRACT Various measures to mitigate the spread of COVID-19 have altered mobility flows worldwide and caused people to adopt new ways of being and moving in public space. These changes have been considerably pronounced across modes of public transportation. This paper explores the experiences of individuals who continued riding and working in public transport throughout the pandemic to yield insight into changing mobility meanings and grounded realities of urban mobility processes in the context of COVID-19 and beyond. Through the combined analysis of ethnographic fieldwork, participant observations and interviews, the paper unpacks lived experiences of riding and working in public transport in the city of Amsterdam during lockdown by addressing the changed nature of embodied encounters and mobile sociability in public transit. Findings denote that COVID-19 has altered the conditions of mobile sociability in spaces of public transport, and has produced complex experiences of daily travel with others involving mutually negative and positive impressions. As a result, we argue that when challenged by COVID-19 related restrictions, mobile sociability and fleeting encounters on the move significantly shape the experience of traveling with others in ways that call into question how we think of public transport as a social space in cities.

An analysis of the role of residential location on the relationships between time spent online and non-mandatory activity-travel time use over time

Although the associations between the use of information and communications technologies (ICT) and individuals' daily travel and activity patterns have been extensively investigated for several decades, few studies have examined the amount of time spent using ICT, its implications on activity-travel behavior, and how such ICT-travel relationships may vary over time and according to residential location. This study takes a quasi-longitudinal perspective to explore how the amount of time individuals spend on the Internet for personal or non-work purposes correlates to their activity-travel for non-mandatory maintenance and leisure purposes, as well as how such associations evolve over a decade. More importantly, it examines how the role of people's residential locations in determining the associations between time spent on the Internet and travel has changed over time. Our approach utilizes two datasets from two major cross-sectional surveys in Scotland: the 2005/06 Scottish Household Survey (SHS) and the 2015 Integrated Multimedia City Data (iMCD) Survey, which were similarly structured and developed. To accommodate the multiple discreteness characterizing activity-travel choice and duration, the multiple discrete-continuous extreme value (MDCEV) model was employed to capture the Internet–travel relationships for the full sample and the urban, town, and rural sub-samples in both 2005/06 and 2015. Our findings suggest that use of the Internet for personal purposes increasingly tends to discourage rather than facilitate physical activity and travel for non-mandatory purposes over time, especially for those who spend high levels of time on the Internet (over ten hours per week). However, such Internet–travel relationships are generally weaker among people living in remote areas than those living in urban areas. While the relationships regarding maintenance activity purposes are significant for almost all levels of Internet users among the urbanites in both years, they were not significantly found at all among rural residents in either 2005/06 or 2015.

Co-evolution of the built environment and travel behaviour in Shenzhen, China

The impact of the built environment on travel behaviour may change over time especially over the course of urban expansion. There is a long-time debate on the existence of a constant time budget that people are willing to spend on daily travel. This study collects data from Shenzhen’s periodical travel surveys spanning two decades and examines the trends in travel distance and travel time as well as the changing impacts of the built environment on travel behaviour. We find that travel distance increases significantly over the study period, but travel time grows at a much lower pace and reaches stable level at the late stage. The travel behaviour impacts of the built environment also change over time in terms of directions and magnitudes. Findings of this study enrich the literature on the built environment-travel behaviour connection with a dynamic perspective and provide evidence supporting the constant travel time budget hypothesis.

Demand, mobility, and constraints: Exploring travel behaviors and mode choices of older adults using a facility-based framework

The steady trend of aging has caused great concern on how cities should better accommodate the social needs of aged population. Older people in general have more leisure time than younger adults but are found highly constrained in daily travel. To examine the imbalance between travel demand and transport supply among older adults, this paper decomposes their daily travel into two categories (visits to non-ubiquitous and ubiquitous facilities) according to major characteristics of travel behaviors using Nanjing Household Travel Survey data. Multinominal logit (MNL) models are applied to exploit the effects of household and personal characteristics, trip characteristics, local supplies, and public transport services on travel mode choices. Results show that (i) travel demand and transport supply are highly unbalanced for most non-ubiquitous facilities, (ii) relative to younger adults, older adults travel further and highly rely on public transport to access non-ubiquitous facilities, (iii) providing more public transit services nearby non-ubiquitous facilities are more reliable to increase the accessibility of older adults than increasing the number of facilities. These results would help policy-makers better understand travel behaviors of older adults and develop strategies to accommodate their travel demand, especially from the perspective of facility network reorganization.

Influence of Mode Use on Level of Satisfaction with Daily Travel Routine: A Focus on Automobile Driving in the United States

How does the extent of automobile use affect the level of satisfaction that people derive from their daily travel routine, after controlling for many other attributes including socio-economic and demographic characteristics, attitudinal factors, and lifestyle proclivities and preferences? This is the research question addressed by this paper. In this study, data collected from four automobile-dominated metropolitan regions in the United States (Phoenix, Austin, Atlanta, and Tampa) are used to assess the impact of the amount of driving that individuals undertake on the level of satisfaction that they derive from their daily travel routine. This research effort recognizes the presence of endogeneity when modeling multiple behavioral phenomena of interest and the role that latent attitudinal constructs reflecting lifestyle preferences play in shaping the association between behavioral mobility choices and degree of satisfaction. The model is estimated using the generalized heterogeneous data model (GHDM) methodology. Results show that latent attitudinal factors representing an environmentally friendly lifestyle, a proclivity toward car ownership and driving, and a desire to live close to transit and in diverse land use patterns affect the relative frequency of auto-driving mode use for non-commute trips and level of satisfaction with daily travel routine. Additionally, the amount of driving positively affects satisfaction with daily travel routine, implying that bringing about mode shifts toward more sustainable alternatives remains a formidable challenge—particularly in automobile-centric contexts.

A Hybrid Model for the Impact of COVID-19 Prevention Measures on the Sustainable Development of the Aviation System

An airport is a complicated system that serves as a vital link in the aviation system. Airplanes are also a popular mode of transportation for daily travel and tourism. Now that COVID-19 is spreading over the world, many airlines are under severe financial strain. Therefore, long-term air traffic planning is critical, especially since the airport is a key region for COVID-19 to expand. To anticipate, simulate, and estimate the medical and health expenditures caused by the propagation of the epidemic under lax, mild, and strict social contact intervention, we utilize a hybrid model of the agent-based model and discrete event simulation. An agent-based model can capture individual behavior, and discrete event simulation can be used to explain the entire boarding process. Limiting the rate of wearing masks while traveling and upgrading airport facilities are the most effective strategies to manage the epidemic, given the convenience of travelers and the swift recovery of the national economy. Simulating passenger boarding will aid government agencies in improving airport equipment and facilities, facilitating passengers’ reasonable travel plans, avoiding large-scale contact, preventing the spread of epidemic diseases, and promoting the long-term sustainability of densely populated public transportation systems such as airports.

Practical Grid-Based Spatial Estimation of Number of Electric Vehicles and Public Chargers for Country-Level Planning with Utilization of GIS Data

This research proposes an approach to estimate the number of different types of electric vehicles for a vast area or an entire country, which can be divided into a large number of small areas such as a subdistrict scale. The estimation methodology extensively utilizes the vehicle registration data in conjunction with Thailand’s so-called EV30@30 campaign and GIS-based road infrastructure data. To facilitate the analysis, square grids are built to form cells representing the number of electric vehicles in any specific area of interest. This estimated number of electric vehicles is further analyzed to determine the energy consumption, calculate the recommended number of public chargers, and visualize an increase in the substation loads from those charging stations. The effectiveness of the proposed methods is demonstrated using the whole area of Thailand, consisting of five regions with a total area of 513,120 km2. The results show that the trucks contribute the most energy consumption while taxis rely on a lot of public chargers. The total energy consumption is about 79.4 GWh per day. A total of 12,565 public fast chargers are needed across the country to properly support daily travel, around half of them being located in the metropolitan area.

Exploring temporal variability in travel patterns on public transit using big smart card data

Passengers generate travel behaviours on public transit, whose variations deserve an exploration with an aim to guide daily-updated managements. In this study, we investigate temporal variability in travel patterns for over 3.3 million passengers across 120 days who use public transit in Beijing. Temporal variability is characterized by a series of features in terms of space coverage, travel distance and travel frequency, based on which, passengers are clustered into two types, that is, commuters with daily travel routines, and non-commuters who do not. How, and to which extent, they change travel patterns over time are examined, with using approaches concerning multivariate regression and curve fitting. Results show that, (1) commuters are more likely to travel longer but cover less territory than non-commuters on weekdays, while the opposite patterns occur on weekends. The variation of day of week affects commuters less, compared to non-commuters, due to more fixed schedules, as expected; (2) travel distance and frequency are found to increase faster, more linearly, than space-coverage features, the last of which experience a progressive decreasing of marginal increases before reaching a plateau. The above findings facilitate transport practitioners to design sound management schemes for passengers in different categories.

Widespread range suitability and cost competitiveness of electric vehicles for ride-hailing drivers

Transportation network companies provide an increasingly significant share of mobility, which has prompted interest in curbing greenhouse gas emissions in the ride-hailing sector. Vehicle electrification offers the possibility of vast emissions reductions, but a number of factors are thought to constrain this transition. We investigate two such factors – battery electric vehicle (BEV) range and total cost of ownership – from 2019 driving data covering all U.S. drivers on the Lyft platform. We estimate that, for more than 86% of drivers, their daily travel needs can be met by a fully charged BEV with listed range of 250 miles (BEV250) on at least 95% of days. New and pre-owned BEVs both appear to be cost-saving for many drivers. We estimate that a $5,700 BEV purchase subsidy would make new BEVs cost-competitive to gas-powered vehicles for all drivers on the Lyft platform, holding annual mileage and vehicle prices constant. Our results suggest that range and lifetime cost should not be significant barriers to widespread EV take-up in the ride-hailing sector. More generally, they suggest that continued moderate subsidies for BEVs, information interventions, and targeting of such programs to ride-hailing drivers who stand to gain most from them will promote a faster transition in this sector. Driver-targeted outreach and information provision related to EV benefits, as well as expansion of charging availability and fast charging rates through local and federal policy, are additional valuable steps to encourage ride-hailing electrification.

Accident responsibility identification model for Internet of Vehicles based on lightweight blockchain

AbstractThe rapid development of autonomous vehicle technology has brought a new experience to people's daily travel. However, if a traffic accident involving autonomous vehicles occurs, it will face difficulties in vehicle accident forensic‐preservation, leakage of vehicle owner's privacy, and identifying legal liabilities. This article proposes an accident responsibility identification model for the Internet of Vehicles based on lightweight blockchain to solve the above problems. This model uses Car Forensics Master to collect evidence from the accident vehicle, and at the same time collects evidence from maintenance service providers, automobile manufacturers, transportation management departments, insurance companies, and other vehicle accident related parties and stores them in the preservation chain. We also use VPKI to protect the autonomous vehicle identity privacy. In order to improve the efficiency of the model and set up authorized access to related entities, the identification of accident liability is jointly completed by the preservation chain and the accident identification chain. In addition, we prove that the protocol proposed in the model has ideal security properties. Finally, we implement the smart contracts in the model through the Solidity language, and evaluate its performance.

Impacts of the walking environment on mode and departure time shifts in response to travel time change: Case study in the multi-layered Hong Kong metropolis

Three-dimensional (3D) transportation space is one of the most important characteristics of multi-layered cities; however, fine-scale built environment factors about facilities that support the 3D walking environment were unavailable in understanding travelers’ behaviors before heading to the Big Data era. Using both small (questionnaire survey) and big (large-scale geospatial) data, we conducted a case study in Hong Kong, a well-known vertical metropolis with a multi-modal transportation system. We analyzed mode and departure time shift behaviors in response to travel time change for 2927 participants and examined the impacts of new variables derived from a 3D pedestrian network dataset and potential influencing factors identified in the literature. The results from the mode shift model indicated that having more efficient pedestrian networks, as measured by the total length of walkways and the provision of mobility aid facilities (e.g., travellators, escalators), is effective in promoting mode shift behaviors. Meanwhile, our departure time shift model revealed that having more comfortable walkways, as measured by travel attitude and the provision of mobility aid facilities, increases users’ willingness to make departure time shifts from peak hours. These findings imply that a more efficient and comfortable walking environment facilitates mode and departure time shift behaviors in daily travel. Improving the building and management of walking environments would contribute to a more integrated multi-modal transport system.

Insights from a pilot investigating the impacts of shared E-scooter use on physical activity using a single-case design methodology

IntroductionShared e-scooters have recently emerged as a convenient, flexible transportation option for short trips in dozens of cities and on university campuses. While there is survey evidence that e-scooting replaces walking and bicycling trips, potentially impacting physical activity (PA) levels, little is known about how e-scooter use objectively affects PA and how e-scooter use affects individual travel options in ways that could influence planning for active transportation. MethodsThis paper provides insights into the PA impacts of shared e-scooter use within a small sample of frequent e-scooter riders in the Phoenix, AZ, area. The single-case, longitudinal study monitored the daily biometric data of eight participants over a five-week period during which e-scooter use was alternately allowed, prohibited, and allowed again in an “ABA” design. The study also employed surveys and daily travel logs to gather information on travel behavior and perceptions of benefits and disbenefits related to e-scooter use. ResultsBiometric data indicate that bicycling and walking trips are substantially more active than e-scooting or driving trips. Self-reported trip data suggest that when e-scooters were allowed (Phases 1 and 3), e-scooter trips disproportionately replaced walking, bicycling, and/or e-biking trips, compared to auto trips. However, when participants were prohibited from using e-scooters (Phase 2), 89% of trips were reported by automobile, complicating the story of trip substitution and suggesting larger contextual influences. Finally, participants saw e-scooters as an important transportation option. ConclusionsE-scooter use appears to reduce physically active travel, although it is somewhat healthier than driving and its relationship with PA seems to be moderated by transportation habits, options, and context. E-scooter users clearly value e-scooters for their convenience, affordability, speed, and fun. Cities could consider restricting e-scooters in highly walkable areas, but should recognize e-scooters as an important part of the urban transportation system.

Multimodal Urban Transportation Network Capacity Model Considering Intermodal Transportation

Transportation network capacity is an important indicator from measuring the performance of a transportation system. Previous studies on the capacity of multimodal networks have not considered transfers between different transportation modes. In fact, because of the diversified development of the urban transportation system, travelers are no longer limited to a single transportation mode in daily travel. This paper thus proposes an improved network capacity model considering intermodal transportation in urban multimodal transportation systems. The proposed network capacity model is formulated as a bi-level programming problem, in which the lower-level model is a combined modal split and traffic assignment (CMSTA) model. The CMSTA model consists of a cross-nested logit (CNL) in the phase of mode split to account for intermodal transportation and a path-size logit (PSL) in the phase of traffic assignment to account for route overlapping. Also, we consider the flow interaction of different modes (e.g., car and bus) when they share the same links. The Barzilai–Borwein step size method is used to efficiently solve the lower-level CMSTA model (i.e., the CNL-PSL model) of which the objective function is complicated. The numerical results demonstrate the advantages and features of the proposed model. The model is also applied to a real network case to assess the network capacity of the multimodal transportation system.

A Statistically Based Model for the Characterization of Vehicle Interactions and Vehicle Platoons Formation on Two-Lane Roads

Two-lane roads are the most significant part of the road network in many countries, and are widely used for systematic and non-systematic daily travels. Traffic on a two-lane road typically involves a high level of interaction among vehicles, with the formation of platoons. As a part of the road network of a country, they represent a crucial development factor from a social and economic point of view, because they ensure the close accessibility of the innermost areas and local markets, and favor the connection between the nodal points of the logistic system and the last mile of the supply and distribution chain. Thus, the estimation of the presence of vehicle platoons makes it possible to develop significant indicators for performance analysis on this type of road, which in turn is the basis for planning, upgrading, and improving transport programs to find a sustainable balance between environmental, social, and economic qualities. This paper presents a statistically based model, the Two-Lane Roads Statistical Platooning Model (TLR-SPM), which allows for evaluating the percentage of vehicles which are free to travel at the desired speed and of non-free vehicles constrained to travel in platoons at lower speeds than desired, as a function of the traffic flow. Based on a data-driven methodological approach, TLR-SPM allows for going beyond the critical threshold value for time headways, such as the widespread 3 s threshold, but lacks the need to hypothesize, identify, or estimate the probability laws for speed and time headway. From the formal treatment of the general statistical method, the paper shows the data processing procedure through its application to a real case. As shown by the application case and the comparisons with the results of other methods, the proposed model can significantly adapt to the experimental data and can support in analyzing a two-lane road in its operating conditions to promote its safety and efficiency as part of a sustainable transport system.