Commute Trips Research Articles

Logit and Probit Models Explaining Mode Choice and Frequency of Public Transit Ridership among University Students in Krakow, Poland

The predictors of urban trip mode choice and one of its important components, public transit ridership, have still not been thoroughly investigated using case studies in Central Europe. Therefore, this study attempts to clarify the correlates of mode choices for commute travel and shopping, and entertainment travel to distant places, as well as the frequencies of public transit use of university students, using a wide range of explanatory variables covering individual, household, and socio-economic attributes as well as their perceptions, mobility, and the nearby built environment. The correlation hypothesis of these factors, especially the role of the street network, was tested by collecting the data from 1288 university students in Krakow and developing Binary Logistic and Ordinal Probit models. The results show that gender, age, car ownership, main daily activity, possession of a driving license, gross monthly income, duration of living in the current home, daily shopping area, sense of belonging to the neighborhood, quality of social/recreational facilities of the neighborhood, and commuting distance can predict commute and non-commute mode choices, while gender, daily activity, financial dependence from the family, entertainment place, quality of social/recreational facilities, residential self-selection, number of commute trips, time living in the current home, and street connectivity around home are significantly correlated with public transit use. Some of these findings are somewhat different from those regarding university students in Western Europe or other high-income countries. These results can be used for policy making to reduce students’ personal and household car use and increase sustainable modal share in Poland and similar neighboring countries.

Passenger Time Use Comparison Between Traditional and Autonomous Vehicles: A Latent Class and Transition Analysis

As the era of autonomous vehicles (AVs) approaches, understanding how passengers’ time use during a trip may change from a traditional vehicle (non-AV) to an AV is important to the adoption and use of AVs. In this study, a latent class analysis (LCA) as well as a latent transition analysis (LTA) are adopted to investigate the choice of travel activities of individuals as passengers in a traditional vehicle, such as a car or transit, and the anticipated shift in these activities in an AV. Since individuals may perform different activities during different trip purposes, activity choices and non-AV to AV transition dynamics are explored from two different perspectives: commute trips (e.g., to work or school) and non-commute trips (e.g., leisure, errands, or medical). Findings from the LCA models show three distinct groups of individuals with varying activity preferences in a traditional vehicle and four distinct groups that could emerge in an AV. AV users exhibited a higher preference for activities such as texting/browsing social media, relaxing, and working, suggesting that AVs may offer passengers a more productive use of their travel time. Furthermore, the LTA model shows that there is a good portion of individuals who were performing one or two activities in a traditional vehicle now becoming variety seekers that could perform at least four different activities in an AV, further corroborating the findings that AVs could provide a more productive and efficient use of travel time.

Time Matters: Analyzing the Impact of Nighttime on Commuter Trip Chaining Behavior and Travel Time Use in Karlsruhe, Germany

Trip chaining and the use of travel time by commuters have long been the foci of considerable research interest. However, despite this attention, few studies have investigated the differences in behavior associated with daytime and nighttime travel. To fill this gap in the literature, the present study investigated the influence of time of day on trip chaining and travel time use. The analysis was based on a data set of residents of the Karlsruhe area who regularly commute both during the day and at night. The data set was analyzed both descriptively and through the application of a series of logistic regression models. The results indicated that nighttime had a significant influence on the formation of trip chains and the use of time during the commute. For example, both the number of trip chains formed and the number of activities performed during the commute are reduced at night. The reasons for forming trip chains also varied according to the time of day. Whereas most trip chains were formed for errands during the day, at night, the majority were formed to pursue leisure activities. In addition, activities such as working, telephoning, looking at the landscape, or reading were significantly reduced at night. The findings of this study contribute to a more in-depth understanding of commuter behavior, on the basis of which, innovative and efficient mobility strategies can be further developed.

Role of private vehicle commuters’ travel wellbeing perception in mode shift behavior towards an upcoming metro in Mumbai Metropolitan region

The expansion of mass rapid transit systems such as the metro in urban metropolitan areas is expected to moderate the problems of congestion and public transport overcrowding. It is crucial to devise measures to attract private vehicle commuters towards these modes to make them economically and financially viable. Positive perceptions and high trip frequency of commute trips can lead to long-term habitual inertia and prevent these commuters from shifting towards new sustainable alternatives. To support transport policy-making in promoting sustainable modes, this study mainly investigated the association of private vehicle commuters’ travel wellbeing perception of their commute, commute frequency, and travel attributes with their mode shift behavior towards an upcoming metro system in Mumbai Metropolitan Region (MMR). Some external changes will occur in the transport system regarding time, cost, and other attributes with the upcoming 426 km metro line network in MMR. A well-designed stated preference survey was conducted, and 2400 observations were collected from 217 private vehicle commuters (two-wheelers and cars). Results indicate that the commuters value metro on-board comfort the most, followed by transfers, travel time, and waiting time. Existing travel wellbeing perception is found to be insignificant in determining the mode shift behavior. Furthermore, regular private vehicle commuters showed a significant shifting propensity towards the upcoming metro. Mann-Whiteney U test results indicate no significant differences in travel wellbeing perceptions among car and two-wheeler commuters.

Quantifying COVID-19′s impact on GHG emission reduction in Oman's transportation sector: A bottom-up analysis of pre-pandemic years (2015–2019) and the pandemic year (2020)

On February 24, 2020, the COVID-19 pandemic hit Oman, forcing nationwide lockdowns and movement limitations. This study estimates the COVID-19 pandemic's impact on Oman's transportation sector's Greenhouse Gas (GHG) emissions. Using a bottom-up methodology, this analysis compares the years preceding the pandemic (2015–2019) to the pandemic year (2020). According to the results, the implementation of COVID-19 lockdown measures significantly reduced GHG emissions from passenger vehicles by 26.4 % and 27.8 % from whole road transportation. The overall GHG reduction from whole road transportation in Oman during the pandemic year (2020) was estimated around 1,502 kilotons CO2eq. These reductions represent 17 % of the pledged of carbon reduction target in the Second Nationally Determined Contributions by 2030. These results highlight the possibility of achieving emission reduction goals through the adoption of remote work practices, which has contributed significantly to the decline in emissions during the pandemic. Remote work has the potential to reduce commuter trips, thereby reducing overall mobility and emissions, particularly in Oman, which lacks public transportation and relies heavily on private vehicles. The potential benefits of remote work in reducing carbon emissions in the transportation sector should be carefully evaluated and encouraged by policymakers in order to help Oman achieve its climate goals.

Understanding and including 'pink-collar' workers in employment-based travel demand models.

The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.

Risky choice and diminishing sensitivity in MaaS context: A nonlinear logit analysis of traveler behavior

In the rapidly transforming landscape of urban transportation, Mobility-as-a-Service (MaaS) has emerged as a potentially transformative solution to adapt to fluctuating travel demand. However, despite arguments in the literature suggesting that uncertainty in travel plans may influence travelers’ preferences for MaaS, the extent and mechanisms of this impact remain relatively underexplored. This study aims to bridge this gap by evaluating travelers’ preferences toward MaaS subscriptions, mode choices, and their reactions to uncertainty within a risky-choice modeling framework. We incorporate a linear-specified benchmark model and two nonlinear models (one based on Expected Utility Theory with Constant Absolute Risk Aversion adjustments, and the other on Prospect Theory), to estimate travelers’ risk perceptions and their valuation of gains and losses associated with travel time and costs caused by occasional activities during commute trips. The data utilized in our study originates from a specifically designed stated preference survey carried out in Hong Kong. Our findings present insightful observations into MaaS bundle configurations, socio-demographic patterns, and notably, behaviors under uncertain circumstances. In the face of uncertainty, we observe a noticeable increase in reluctance to adopt MaaS, and a heightened preference for taxis. The models also underscore significant risk aversion and diminishing sensitivity concerning travel time and cost, with these tendencies proving more pronounced for travel time compared to cost. These insights are valuable for policymakers and MaaS operators, offering an enhanced understanding of users’ risk attitudes and the intricate interplay between travelers’ preferences for MaaS and the uncertainties inherent in daily travel.

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19

Using Geographically Weighted Models to Explore Temporal and Spatial Varying Impacts on Commute Trip Change Resulting from COVID-19

Examining the long-term reduction in commuting emissions from working from home

To develop effective climate strategies, it is necessary to model the long-term impacts of combined policy measures. This study examines how an increase in working-from-home (WFH) practices, coupled with higher private car fleet penetration of electric vehicles (EVs), could change commuting patterns and associated emissions. Simulations for the Dublin Region show that if half of white-collar workers were WFH and EVs made up one-third of the fleet as forecasted for 2030, emissions from travel activities could be reduced by up to 35% for carbon dioxide (CO2) and 25% for particulate matter (PM). However, transitioning from a moderate to a high WFH scenario may not deliver significant benefits in terms of travel length, modal shift, and emissions reduction. In addition, a decrease in commuter trips can lead to an increase in other trips. This suggests that there is a need for additional measures to discourage car usage when commuter trips decline.

EVALUASI KINERJA LALU LINTAS PEMBANGUNAN UNDERPASS CIBITUNG KABUPATEN BEKASI DENGAN MENGGUNAKAN APLIKASI VISSIM (Kawasan Pasar Induk dan Stasiun Cibitung Kabupaten Bekasi)

Bekasi District is one of the areas of the national economy that has a significant influence on the industrial sector. A large number of generation and attraction areas occur in the Main Market and Cibitung Station areas. The main market is a generation and pull area for people from inside or outside Bekasi Regency, while the Cibitung Station area is a new zone that is the generation and pull area for Jabodetabek commuter trips. One of the improvement activities in Bekasi Regency is the construction of the Cibitung Underpass. This underpass is located on Jalan Bosih Raya as a solution to traffic congestion in the Cibitung Station area. The existence of this underpass should provide a solution to congestion in the Pasar Induk and Cibitung Station areas, but if seen at this time the traffic conditions in the Cibitung Main Market Area are still experiencing congestion. The method used in this research is to create a validated traffic network model with the help of Vissim software. The model is then used to determine the performance of traffic sections and intersections both in terms of current conditions and in terms of the handling process. the optimization of the intersections were calculated using the MKJI model. Traffic performance of road sections and intersections will be obtained from alternative treatments. The expected research result is to get the best alternative that is able to reduce the value of queues and delays in the study area.

Do Open-ended Questions Influence the Measurement of Attitudes? An Investigation

Measuring attitudes is gaining even more importance in travel behavior research. Previous research indicates that the open-ended questions prime closed-ended responses. In this research, we use a questionnaire that analyses the stated choice of use of autonomous vehicles (AV). Attitudes are measured using an extended version of the Technology Acceptance Model. Two questionnaire versions were used to collect data from 2000 respondents from the USA. An integrated latent variable and choice model was estimated to predict the intention to use AVs for commute trips. The models’ performance along with the estimated for the two datasets were compared.

Calibrating walkability indicators for commute walk trips: A structural equation modeling approach

Walking plays a significant role in promoting public health, decreasing air pollution, and vitalizing cities. Walkability is an important notion that transportation planners and policymakers often use to quantify the suitability of and to promote travelers’ propensity for walking in an urban area. This paper adopts a data-driven approach using structural equation modeling for simultaneous estimation of 1) a latent construct of walkability using different walkability indicators (thus estimation of weights of the indicators), and 2) the effect of walkability on commute walking behavior for an urban area. Doing so accounts for the effects of various indicators of walkability as well as socio-economic and security characteristics. A Partial Least Square Structural Equation Model is developed to estimate both direct and indirect effects of walkability on the share of commute walk trips in total commute trips for the city of Chicago using census tract-level data. We find that walkability is the most important factor affecting the share of commute walk trips. Among the indicators of walkability, transit- and job density-related indicators are the most important ones. Some socio-economic factors, especially travel time, vehicle ownership, and college student share, also critically influence the propensity for walking to commute. We further find that our calibrated walkability score results in a different hotspot distribution in Chicago than using the uncalibrated walkability score, as is currently practiced by the Chicago Metropolitan Agency for Planning, but is more consistent with hotspot distribution of the actual commute walk trips in the city. These findings advance the understanding and characterization of walkability and actual walking behavior in urban areas, which in turn helps inform pedestrian infrastructure investment and policy making.

Examining commute mode choice of essential workers before and during the COVID-19 pandemic – A case study of the University of Washington

During disruptive events and major crises such as the COVID-19 pandemic, essential workers, defined in this study as employees who must continue to make frequent commute trips, play a critical role in maintaining the basic function of society. It is therefore vital for transportation planners and policymakers to ensure that adequate mobility services are provided to meet the travel needs of this group of workers. To better understand essential workers’ mobility barriers and inform future transportation policy, this research examines their pre- and during-pandemic commutes, as well as prospects on future commuting, using data from the Transportation Needs Assessment Survey conducted by the University of Washington in Seattle. The results show that, first, over 60% of pre-pandemic public transit riders switched to other modes, especially driving alone. In contrast, almost all the essential workers who drove alone, biked, or walked before the pandemic continued to do so during the pandemic. Second, the shift to driving alone was most pronounced among essential workers with high incomes, whereas public transit remained as a primary mode choice of lower-income groups. As travel distance increased, the probability of driving alone over public transit also went up, although the relationship was not linear. We did not find consistently significant associations between mode choice and other sociodemographic variables. Finally, our analysis indicated that most essential workers anticipated to return to their pre-pandemic commute frequency and mode choice when the pandemic is under control. Transportation planners and policymakers can facilitate mobility for essential workers through planning and policy innovations, especially during disruptive events and major crises.

Analysis of trip frequency choice of commute trips in the context of COVID-19 in India: A hybrid choice modelling approach with generalized ordered logit kernel

The paradigm shift in mobility and travel behaviour caused by the successive waves of the COVID-19 pandemic has been unreal. The long-term effects of the pandemic resulting from the fear of the spread of the virus against the belief in the remedial measures are to be understood from a behavioural perspective to strengthen the current transportation system against such impediments. The current study adds to the literature on COVID-19 pandemic by unravelling the long-term impacts of the pandemic on the trip frequency of commute trips. A dataset of 467 individuals from all over India is analysed to understand the factors impacting the willingness of the respondents to reduce their commute trips in a post-vaccinated scenario. An integrated choice and latent variable structure, with a generalized ordered logit kernel, was considered to incorporate the influence of psycho-attitudinal variables and socio-demographics on the willingness to reduce trip frequency among individuals. The results indicate a significant impact for variables including fear of the virus spread, age of individuals, and job satisfaction of working from home on the stated willingness towards trip reduction. The study concludes by presenting policy measures that target to overcome the effects of the pandemic and restore normalcy.

Does the information provider choose to promote public transport use or protect public health? A case study in China

BackgroundHealth risks from air pollution require cutting motorized private vehicle use. Providing new public travel patterns, along with appropriate smartphone-based multimodal travel information services (SMTIS) is considered an effective way to alleviate this stress. However, air pollution is not likely to be solved overnight. There is a growing demand for more detailed air pollution information accessible through mobile apps to avoid exposure to pollution, which may increase the use of private cars. So far, little is known about this contradictory behavior. MethodsBased on 1926 observations obtained from a stated preference experiment conducted in Shanghai, a mixed logit model was developed to investigate the effect of SMTIS on travelers’ mode choice behavior. Specifically, this study aims to reveal the impact of progressively providing typical multimodal information, social influence information, and air pollution information expressed in cigarette equivalents on commuters’ choice of travel modes, including private car, park-and-ride (P&R), and customized bus. ResultsInformation offered by SMTIS, including travel time, travel cost, available parking spaces, comfort level, and air pollution information, exerts significant influences on travel mode choice. Also, there is observed taste heterogeneity for such information across the population. Respondents placed less emphasis on air pollution information during commute trips than on travel time. Providing information on modal pollution exposure prevents people from using public transport. Even when air quality is good, we still observe an increase in the proportion of people choosing cars. With regard to social influence information, people were more likely to choose P&R when online word-of-mouth about P&R displayed a positive propensity, while the percentage of other travelers who had selected P&R did not exhibit a significant effect. ImplicationsThe findings provide valuable implications for the design of low-carbon traffic management policies and the deployment of multimodal information services.

Terra Incognita: California Transit Agency Perspectives on Demand, Service, and Finance in the Age of COVID-19

The COVID-19 pandemic upended transit use, finance, and management. To investigate these effects two years into the pandemic, we conducted 21 semi-structured interviews with senior managers at transit agencies in the most populous U.S. state, California. We found that the pandemic generated many operational and managerial challenges for transit agencies. Ridership plummeted, then slowly recovered, but is still well below pre-pandemic levels at most agencies. Commuter trips to and from major job centers were especially slow to return. In response to decreased demand, public health concerns, and uncertain finances, many agencies cut services and spending early on. As a result, fare revenues declined, in some cases precipitously. However, federal pandemic relief funds proved essential in filling budgetary gaps, stabilizing finances, preventing layoffs, and maintaining services. Other transit subsidies mostly bounced back robustly. Our interviews suggest that, though California transit agencies experimented with free fares, few fareless programs were made permanent. Their challenges include considerable uncertainty associated with future travel demand, looming financial shortfalls at systems that formerly had high farebox recovery and are still drawing on federal pandemic funds to backfill their fare revenue losses, and protracted labor shortages of drivers and mechanics that are preventing many systems from providing desired levels of service.

Shifting car travel to active modes to improve population health and achieve transport goals: A simulation study

IntroductionBeing physically active has multiple health benefits and contributes to the reduction of co-morbidities and mortality from chronic diseases. Active transport (walking and cycling) contributes to population health by enabling physical activity. We developed a simulation model to measure health impacts of transport scenarios for Melbourne, Australia. Our aim was to demonstrate active transport health impacts and support the materialization of policies for healthy cities and people. The model measures health impacts of increased physical activity from replacing short car trips for any purpose or for commuting under 5 km by walking and cycling. MethodsWe developed a micro-simulation model of physical activity and disease risk in combination with the well-established proportional multi-state life table model. We quantified life course health including health adjusted-life years, life years, new cases of diseases prevented, and deaths prevented for 14 chronic diseases associated with physical inactivity for the adult population of people from Melbourne, Australia in 2017. ResultsOver the life course of the Melbourne adult population of 3.6 million people in 2017, gains in health-adjusted life years ranged from 5,100 (95% Uncertainty Interval (UI) 3,700 to 6,500) for the scenario replacing commute trips by car under 1 km with walking up to 738,800 (95% UI 546,000 to 935,000) when replacing car trips under 2 km with walking and between 2 km and 5 km with cycling. We also estimated benefits in terms of reductions of new cases of diseases and deaths prevented, with the greatest gains for ischemic heart disease, stroke, Alzheimer's and other dementias and type 2 diabetes. ConclusionsWe found that shifting car travel to active modes would accrue important health benefits for the 2017 Melbourne population. Our results support policies and strategies for sustainable transport planning to contribute to reduce the burden from chronic diseases and environmental impact of car-oriented cities.

Exploring the potential of shared electric vehicles from e‐mobility hubs as an alternative for commute and food shopping trips

AbstractElectric shared mobility hubs, called eHUBs, offer users access to a range of shared electric vehicles, including e‐bikes, e‐cargobikes, and e‐cars. Through the diversity of modes offered, eHUBs provide mobility solutions for different target groups and trip purposes. In this study, potential users’ willingness to use shared electric vehicles from eHUBs as either a commute or food shopping trip alternative was analysed using logistic regression methods. Results indicated that half of respondents were willing to use shared electric vehicles for at least a few of their regular commute or food shopping trips, although this proportion dropped substantially if considering the use of shared vehicles in combination with public transport. Across modes and trip purposes, holding a pro‐shared mobility attitude and belonging to the youngest age group strongly increased the willingness to use shared modes. Yet, while eHUBS may offer a potential alternative for at least some of people's regular commute or food shopping trips, cross‐mode shifts may be limited. That is, car drivers show a greater interest in shared e‐cars, whereas cyclists show a greater interest in e‐bikes and e‐cargobikes with public transport. Further influential factors, as well as implications for both shared mobility providers and local authorities, are discussed.

Estimating Travel Choice Probability of Link-Based Congestion Charging Scheme for Car Commuter Trips in Jakarta

The likely effects on car commuters of enforcing congestion charging using the link-based corridor method include that they may shift to public transport, divert their route of travel, or decide not to travel to the related area. However, most recent research has focused mainly on the choice of modes. This paper examined the travel choices of private car commuters resulting from the congestion charging scheme set to be implemented in Jakarta, Indonesia. The scheme is intended to replace the current odd-even strategy. It is imperative to study all possible mutually exclusive alternatives faced by car commuters. A web-based e-survey was used for data collection, employing the stated preference method. The discrete choice multinomial logit model was chosen to analyze the data. A total of 401 of the 2125 respondents to the e-survey questionnaire link, evenly distributed to all areas of Greater Jakarta, were sampled in this study. The sample respondents who traveled by car, passing through the eight designated corridors, were analyzed. NLOGIT6 software was used to analyze the parameter of attributes, the probability of alternatives chosen, and the marginal effects of congestion charging on such corridors, employing the multinomial logit model (MNLM). One surprising finding was that the load factor and taxi fares were not significant, indicating that the level of in-vehicle overcrowding is not a concern of respondents, and taxi services are not a substitute for car travel. Another surprising finding was that income variables and job type do not significantly influence travel behavior. In terms of the probability of commuters to continue to travel by car when link-based congestion charging is imposed, only around half of the car travelers were willing to pay the congestion levy and pass through congestion charging corridors. The probability of car travelers diverting onto alternative roads is high, i.e., around 16.82% to 22.88%, while the probability of car travelers shifting to mass transportation is 17.69%. When interpreting direct marginal effects, there is a change in the probability of all travelers choosing to use private cars through the congestion charging corridor of −0.0338, or a decrease of −3.38% for every IDR 1000 increase in the congestion charging levy rate, ceteris paribus.

Optimization of Consecutive On-Ramp Control for Urban Freeways: An Application of the Store-and-Forward Approach

Highway congestion is a common problem worldwide, and different management strategies have been proposed and implemented. In Taiwan, commuter trips in urban areas tend to use freeways for daily trips. One particular problem in Taiwan is consecutive on-ramps within metropolitan areas, such as Taipei, Taichung, and Kaohsiung. Three to four on-ramps bring more vehicles to the freeways, and coordinating with an integrated ramp control strategy is critical to avoiding congestion in metropolitan areas. Based on the concept of the store-and-forward method, this study proposes a linear optimal control model to optimize integrated on-ramp control strategies. The store-and-forward method is chosen because freeway congestion is usually observed in these areas, and freeway efficiency depends on avoiding queue length. The developed mathematical program can be solved efficiently through mathematical optimization packages. The objective of the formulation is to minimize the total queue length within considered areas. In the Taipei metropolitan area, numerical experiments were conducted based on three consecutive on-ramps, including Yuanshan, Taipei, and Sanchun ramps. Several scenarios were designed to illustrate the possible benefits of the proposed model.