Transit Users Research Articles

Characteristics of Zero-Vehicle Households Among Older Americans and Their Travel Implications

Problem, research strategy, and findings As the number of older Americans increases rapidly and many of them age in low-density areas, understanding their transportation difficulties and needs is crucial. Using the 2017 National Household Travel Survey, I categorized zero-vehicle older individuals into four groups: urban commuters, nonrural low-income nondrivers, rural unemployed nondrivers, and nonurban moderate transit users. I then examined who they are and how they travel. More than one-third of zero-vehicle individuals were rural unemployed nondrivers. They mostly lived in rural areas and towns without readily available public or private transportation services and thus made the fewest trips among all zero-vehicle groups. In contrast, urban commuters—who mostly lived in metropolitan areas with rail systems and were healthy—had more experience using public transit and did not report any travel disadvantages compared with those using vehicles. Nonrural low-income nondrivers, however, manifested travel difficulties, largely due to their relatively poor health and income constraints. Takeaway for practice Planners and policymakers should consider providing additional transportation services and neighborhood designs for older people without vehicles. They should offer transportation services in suburban and rural areas, as well as in small towns lacking convenient public transport services. For older people who still drive, provision of both vehicle and safety support for cars and roads should be of paramount concern, because this assistance can help such individuals remain drivers longer. They should also provide affordable and accessible transportation solutions for older people, because income and health are two main obstacles in daily travel for older people without vehicles. In the long run, planners should muse over land use and design changes in low-density areas to facilitate older people’s out-of-home activities.

A study on the factors affecting the adoption of electric buses among the non-Public Transit users in Ahmedabad, India

A study on the factors affecting the adoption of electric buses among the non-Public Transit users in Ahmedabad, India

Impact of Regional Structure and Topography on the Effectiveness of Public Transportation Services

This study investigates the impact of urban structure and topography on public transportation effectiveness in developing countries. Using structural equation modeling analysis of survey data from 500 public transit users across five diverse cities, we examine how these geographical factors influence accessibility, reliability, comfort, and user satisfaction. Results indicate that both urban structure and topography significantly affect transportation effectiveness, with urban structure having a slightly stronger influence. Polycentric urban designs and moderate topography were associated with higher public transit effectiveness. User perceptions highlighted the importance of integrated planning approaches. The findings suggest policymakers should consider geographical contexts when designing public transportation systems, potentially through adaptive infrastructure and context-specific service models. This research contributes to a more nuanced understanding of public transit planning in varied urban environments of developing nations.

Efficiency in public transportation: a new flow direction method for optimizing multi-route networks

ABSTRACT Transit users generally travel between different pairs of stops on the transit line. Therefore, decision-makers should consider the route designs for each passenger’s benefit. In this study, the factor of the number of public transit passengers is combined with trip coefficients such as the route circuity coefficient used to determine the efficiency of public transportation (PT) vehicles. The flow direction performance (FDP), a novel criterion based on the circuity coefficient in PT, is developed. Accordingly, the flow direction method (FDM) a new hybrid metaheuristic optimization method that determines the optimum PT routes, is created. FDM calculates the optimum routes between several predetermined origin points and terminal points. Both sequences of stations and the transit route are optimized at the same time. We tested FDM’s performance on 2 benchmark and 3 real case network samples. Test results show the effectiveness of FDP and robustness of FDM on multi-route problems.

Assessing the Effect of Negative Externalities of Urban Transport on Travel Satisfaction for Work Trips

Research on the importance and performance of work trip attributes for users of different transportation modes—such as public transit, two-wheelers, and four-wheelers—and their impact on travel satisfaction remains limited. This gap is particularly evident when considering the influence of negative externalities of urban transport on travel satisfaction in developing countries. This study seeks to fill this gap by performing an importance-performance analysis of work trip attributes and evaluating the impact of these attributes on travel satisfaction through the use of an ordered hybrid choice model. Findings underscore the significant impact of negative externalities on work trip-related travel satisfaction across all mode users. Additionally, a higher travel cost is negatively associated while a lower travel time is positively associated with travel satisfaction. Higher-income individuals and four-wheeler users exhibit relatively high travel satisfaction compared with two-wheeler and public transit users. Furthermore, the analysis of work trip attributes reveals that the most crucial factors are the access time for public transit users, travel time reliability and travel time delay for four-wheeler users, and the risk of road crashes for two-wheeler users. The study's findings offer insights for policymakers and planners when prioritizing strategies within the urban transport sector in the Mumbai Metropolitan Region (MMR). On a global research scale, the study advocates for the inclusion of negative externalities in travel satisfaction research, emphasizing their pivotal role in shaping the travel experience.

Joint deployment of exclusive bus lanes and limited access bus lanes in a transportation network

Providing exclusive right-of-way to buses is considered an effective measure to enhance the transit system’s performance. However, the conversion of a General-Purpose Lane (GPL), which is shared by all transportation modes, to an Exclusive Bus Lane (EBL), reduces the available capacity for auto users and may result in an underutilized lane known as the empty syndrome lane. To address this deficiency, it is suggested that a special class of vehicles, such as High Occupancy Vehicles (HOVs) be permitted to drive in EBLs, but they may be required to pay tolls. This type of lane is called a Limited Access Bus Lane (LABL) and is designed to prioritize public transportation and restrict the entry of HOVs in bus lanes. The goal is to ensure transit passengers do not experience a decrease in the quality of service. This paper compares the results of four bi-level optimization models used to determine the optimal location of different combinations of special-use lanes in the network, namely: 1) only EBLs; 2) joint deployment of EBLs and HOV lanes; 3) joint deployment of EBLs and tolled HOV lanes; and 4) joint deployment of EBLs and LABLs. A base optimization problem is developed for the last proposed scheme (i.e., joint deployment of EBLs and LABLs), and the formulation is modified to solve three other network design problems. The proposed model is solved by applying a genetic algorithm. To analyze the suggested framework, a numerical test is conducted on the Sioux Falls network as a medium-sized network and the network of Zanjan as a real-sized network. Furthermore, by analyzing the numerical results of the four optimization problems, this study highlights that the joint deployment of EBLs and LABLs outperforms the other three schemes in terms of transit users’ travel time and the average volume capacity of bus routes.

Modal Shift Potential of Different Mode Users Due to Introduction of the First MRT in Dhaka: A Prelaunch Study

Dhaka city is experiencing tremendous growth in traffic. Until recently, the city’s demand for traffic was entirely served by public buses, a mix of motorized and non-motorized paratransit, and private personalized transport. The first ever rail-based metro, mass rapid transit (MRT), namely MRT Line 6, was partially inaugurated on 28 December 2022. Authority expects that there will be visible modal shift. However, MRT systems in many Asian and European countries are attracting much lower private motorists than what is expected. Moreover, in Dhaka, a unique mix in road-based transport with public transit and varieties of paratransit and private personalized vehicles intensifies the uncertainties involved in modal shift. Therefore, based on a field survey done before four (04) months of the partial inauguration of MRT Line 6, this study intends to explore the modal shift potentials of different mode users to a completely new mode and the modal choice factors. The analysis of the results finds overall, 75% of all mode users are willing to shift, while para and public transit users are comparatively more willing than private personalized vehicle users. However, such willingness comes up with one or more conditions: comfort, reasonable fare, reduced total travel time, less crowd, etc. The study finds that affordability, availability, and accessibility factors have an influence on their modal shift behavior. Also, results from binary logistic model identify significant impact of several sociodemographic, trip- and accessibility-related factors influencing modal shift choice. Findings from this study explain the optimism regarding MRT by different mode users and inform the decision-makers about their course of actions including different interventions, strict and carrot approaches to hold on to the potential shifters and attract more.

Understanding the urban mobility challenge: Why shared mobility providers fail to attract car drivers

Reducing urban car traffic has become a global policy objective. A vital element in achieving this objective is the availability of shared mobility providers offering sustainable alternative travel modes such as micromobility, carsharing, and ridehailing. Despite policy and industry efforts, shared mobility options have failed to change the travel patterns of current car users. We explore this urban mobility challenge through an empirical study of 807 urban travelers. Our results show that car drivers estimate travel costs conceptually rather than rationally and consider both trip purpose and length. In particular, car users perceive longer and recreational trips as cheaper per distance driven than shorter and work-related trips. A segmentation analysis reveals that habitual and technology-averse car drivers are particularly susceptible to this skewed cost perception, unlike more frequent transit users. Embedding these results in a case study of current shared mobility options shows that these options are ill-adjusted to the preferences of car travelers but more attractive to current transit users, potentially leading to cannibalization. Based on these findings, we outline several strategies for mobility providers and policymakers to attract car drivers to shared mobility options, thereby contributing to solving the urban mobility challenge.

The influence of residential location and public transit options on commuters’ intention to use travel apps for different travel purposes

While living in city centers is usually linked to higher accessibility levels, shorter travel times, and higher levels of public transit (PT) utilization, the opposite is true for residents of suburban areas. This assumption holds in metropolitan contexts, where central areas offer better accessibility and are associated with higher levels of PT use. In metropolitan peripheries, a large part of commuting is done on an interurban basis, so that the level of use of public transit can be linked to the supply and information available. This work aims to understand the conditions in terms of transit supply and land use, considering the most frequent trip, psychological variables, and the modal choice of commuters’ motorized modes, in the intention to use the new real-time multi-modal travel app, such as advanced traveler information systems (ATIS) for digital mobility-management assistance. A Structural Equation Model is developed to empirically test a sample of 768 respondents collected in two suburban corridors in the Lisbon Metropolitan Area (LMA). Finally, our findings indicate that residential location, mode choice, and trip complexity have a relevant influence on the intention to adopt travel apps. Male students belonging to Generation Y/Z are the most likely users of travel apps. Regardless of the reason, travel patterns associated with more complex (more transfers) and more frequent trips can reinforce the intention to use apps. Also, it is worth noting that students are frequent public transit users, and public transit is also related to trip complexity.

Analyzing the reporting error of public transport trips in the Danish national travel survey using smart card data

AbstractHousehold travel surveys have been used for decades to collect individuals and households’ travel behavior. However, self-reported surveys are subject to recall bias, as respondents might struggle to recall and report their activities accurately. This study examines the time reporting error of public transit users in a nationwide household travel survey by matching, at the individual level, five consecutive years of data from two sources, namely the Danish national travel survey (TU) and the Danish smart card system (Rejsekort). Survey respondents are matched with travel cards from the Rejsekort data solely based on the respondents’ declared spatiotemporal travel behavior. Approximately, 70% of the respondents were successfully matched with Rejsekort travel cards. The findings reveal a median time reporting error of 11.34 min, with an Interquartile Range of 28.14 min. Furthermore, a statistical analysis was performed to explore the relationships between the survey respondents’ reporting error and their socio-economic and demographic characteristics. The results indicate that females and respondents with a fixed schedule are in general more accurate than males and respondents with a flexible schedule in reporting their times of travel. Moreover, trips reported during weekdays or via the internet displayed higher accuracies compared to trips reported during weekends and holidays or via telephone interviews. This disaggregated analysis provides valuable insights that could help in improving the design and analysis of travel surveys, as well accounting for reporting errors/biases in travel survey-based applications. Furthermore, it offers valuable insights underlying the psychology of travel recall by survey respondents.

Bus travel feature inference with small samples based on multi-clustering topic model over Internet of Things

With the widespread application of Internet of Things (IoT) technology, there has been a shift from a broad-brush to a more refined approach in traffic optimization. An increasing amount of IoT data is being utilized in trajectory mining and inference, offering more precise characteristic information for optimizing public transportation. Services that optimize public transit based on inferred travel characteristics can enhance the appeal of public transport, increase its likelihood as a travel choice, alleviate traffic congestion, and reduce carbon emissions. However, the inherent complexities of disorganized and unstructured public transportation data pose significant challenges to extracting travel features. This study explores the enhancement of bus travel by integrating advanced technologies like positioning systems, IoT, and AI to infer features in public transportation data. It introduces the MK-LDA (MeanShift Kmeans Latent Dirichlet Allocation), a novel thematic modeling technique for deducing characteristics of public transit travel using limited travel trajectory data. The model employs a segmented inference methodology, initially leveraging the Mean-shift clustering algorithm to create POI seeds, followed by the P-K-means algorithm for discerning patterns in user travel behavior and extracting travel modalities. Additionally, a P-LDA (POI-Latent Dirichlet Allocation) inference algorithm is proposed to examine the interplay between travel characteristics and behaviors, specifically targeting attributes significantly correlated with public transit usage, including age, occupation, gender, activity levels, cost, safety, and personality traits. Empirical validation highlights the efficacy of this thematic modeling-based inference technique in identifying and predicting travel characteristics and patterns, boasting enhanced interpretability and outperforming conventional benchmarks.

TripChain2RecDeepSurv: A novel framework to predict transit users’ lifecycle behavior status transitions for user management

Transit users’ lifecycle behavior pattern transition reflects the continuous and multi-phase changes in how frequently and regularly users utilize public transit over their lifetime. Predicting transit users’ lifecycle behavior pattern transition is vital for enhancing the efficiency and responsiveness of transportation systems. Thus, this study incorporates lifecycle analysis in predicting long-term sequential behavioral pattern transition processes to go beyond just examining user churning at a single point in time. Specifically, this study proposes the TripChain2RecDeepSurv, a novel model that pioneers the individual-level analysis of lifecycle behavior status transitions (LBST) within public transit systems. The TripChain2RecDeepSurv is composed of (1) the TripChain2Vec module for encoding transit users’ trip chains; (2) the self-attention Transformer module for exploring the latent features related to spatiotemporal patterns; (3) the recurrent deep survival analysis module for predicting LBSTs. We demonstrate TripChain2RecDeepSurv’s predictive performance for empirical analysis by employing Shenzhen Bus data. Our model achieves a 74.39% accuracy rate in churn determination and over 80% accuracy in status sequence identification on the churn path. In addition, our findings highlight the segmented nature of Kaplan-Meier curves and identify the optimal intervention time against the user churning process. Meanwhile, the proposed model provides individual-level heterogeneity analysis, which emphasizes the significance of customizing user engagement strategies, advocating for interventions that extend users’ engagement in patterns with high-frequency transit usage to curb the transition to less frequent travel usage.

Travel behaviour changes among young adults and associated implications for social sustainability

Travel behaviour changes among today’s young adults in relation to important life events, and associated implications for environmental sustainability have been discussed in recent studies. However, the social implications of their travel behaviour changes remain less explored. Applying latent transition analysis on longitudinal data collected in 2019 and 2022, we examined travel behaviour changes among young adults in the Greater Toronto and Hamilton Area, Canada in response to two life events- start of employment after completion of education and residential relocation and explored their social sustainability-related implications in terms of potential transportation-related social exclusion. Young adults showed a higher probability of becoming car users in 2022, however, those who joined workforce and relocated to urban areas were more likely to become transit users and active modes users. Young adults who switched away from active modes to public transit participated in fewer activities and were more likely to experience transportation-related social exclusion. Implications for transportation planning and policy have been discussed.

Investigation of modal integration for transit-access trips in motorcycle dependent cities—The case study of Hanoi, Vietnam

Micro-mobility has the potential to address first -mile challenges, improving transit accessibility and encouraging public transit usage. However, users’ acceptability of modal integration between various micro-mobility options and public transit remains largely unexplored in the literature. Our study investigates the user behavior for first-mile options, focusing on four alternatives: walking, bicycling, motorcycling, and bus, to access urban mass rapid transit (UMRT) in Hanoi, Vietnam. Based on data collected from 1380 individuals, a Nested Logit Model (NLM) was proposed to analyze the determinants of users’ acceptability under each access mode option as well as evaluate further impacts of shifts in access mode choice on vehicle-kilometer traveled and emissions. The analysis shows that the availability of access modes might increase UMRT use by 47.83%. While this increase further generates additional vehicle-kilometer traveled due to the increase in park-and-ride users, this is offset overall by the large number of motorcycle users shifting to UMRT. Under the most optimistic scenario, modal integration for transit-access trips leads to an average reduction of 17.7% in net vehicle-kilometer traveled or 14.5% in net CO2 emissions or 10.9% in NOx from private vehicles. Our findings also imply that the introduction of parking fees for bicycling- or motorcycling-access trips, while impactful, does not significantly change UMRT choice. Therefore, the pricing schemes should be a focus of parking planning surrounding stations. Finally, a number of policy suggestions for parking planning and first-mile vehicles are presented.

Cooperating Graph Neural Networks With Deep Reinforcement Learning for Vaccine Prioritization.

This study explores the vaccine prioritization strategy to reduce the overall burden of the pandemic when the supply is limited. Existing vaccine distribution methods focus on macro-level or simplified micro-level assuming homogeneous behavior within populations without considering mobility patterns. Directly applying these models for micro-level vaccine allocation leads to sub-optimal solutions. To address the issue, we first proposed a Trans-vaccine-SEIR model to incorporate mobility heterogeneity in disease propagation. Then we develop a novel deep reinforcement learning to seek the optimal vaccine allocation strategy for the disease evolution system. The graph neural network is used to effectively capture the structural properties of the mobility network and extract disease features. In our evaluation, the proposed framework reduces 7%-10% of infections and deaths compared to the baseline strategies. Extensive evaluation shows that the proposed framework is robust to seek the optimal vaccine allocation with diverse mobility patterns. In particular, we find transit usage restriction is significantly more effective than restricting cross-zone mobility for the top 10% age-based and income-based zones under optimal vaccine allocation strategy. These results provide valuable insights for areas with limited vaccines and low logistic efficacy.

A regression-content analysis approach to assess public satisfaction with shared mobility measures against COVID-19 pandemic

IntroductionThe transportation sector was severely impacted by the COVID-19 pandemic, with shared mobility services being the most affected due to concerns from the public regarding the high likelihood of being a vector of the virus. Although studies have evaluated the impact of the COVID-19 pandemic on shared mobility, a deeper understanding of public satisfaction with the measures adopted during COVID-19 has not been explored. MethodsThis study utilized data collected in the Summer of 2020 across the United States to fill that literature gap. The study applied Ordered Probit (OP) models to explore the factors influencing an individual's confidence in not contracting COVID-19 while using shared mobility modes and Text Network Analysis (TNA) to understand the deeper reasons for their confidence levels. ResultsResults show a significant influence of sociodemographic factors, land-use/built environment, pre- and post-COVID travel behavior, and activity participation on respondents’ level of confidence for not contracting COVID-19. Only frequent public transit users showed that they have high confidence in not getting COVID-19 when they use any of the shared mobility options, while people who did not use public transit and those who frequently attend telehealth meetings had low confidence in the measures adopted by shared mobility providers. Furthermore, the text mining results indicated that cleanness was the key theme regardless of the confidence level of the respondents, except for rail and bus transit. However, we observed other patterns of themes across the types of shared mobility. ConclusionsThe study findings can be beneficial in the future to improve ridership during pandemics by considering perceptions and satisfactions of various users.

Evaluating accessibility benefits and ridership of bike-transit integration through a social equity lens

Integration of bike usage and transit services is an effective way to enhance accessibility for both transportation modes. Using high-resolution transit data, the study analyzes bike-transit multimodal accessibility and usage patterns through a social equity lens. Two types of accessibility increment are studied: bicycle increment to public transit – the benefits of using bicycle for transit riders, and transit increment to cycling – the merits of using transit for cyclists. Results show that bike-transit integration benefits both public transit riders and cyclists, expanding their accessible opportunities by up to 70% and enabling longer trips for cyclists while providing continuous benefits for public transit users. Meanwhile, better infrastructure significantly improves multimodal accessibility, resulting in more increment for public transit riders but less increment for cyclists. The paper also shows the spatiotemporal patterns of multimodal ridership. The research highlights disparities in bike-transit activities for Black communities due to inadequate local biking infrastructure. Black people majority neighborhoods enjoy less increment compared to other neighborhoods for shorter and very long trips; they also have disproportionately lower multimodal ridership despite much higher transit ridership and better transit access. Enhancing biking infrastructure in these areas can improve physical accessibility increment and promote social equity. The paper provides practical insights for transit planning, emphasizing the importance of connecting bike lanes and creating safer streets for cycling.

Reduced seasonal coronavirus incidence in high-risk population groups during the COVID-19 pandemic.

Epidemiological data on seasonal coronaviruses (sCoVs) may provide insight on transmission patterns and demographic factors that favor coronaviruses (CoVs) with greater disease severity. This study describes the incidence of CoVs in several high-risk groups in Ottawa, Canada, from October 2020 to March 2022. Serological assays quantified IgG and IgM antibodies to SARS-CoV-2, HCoV-OC43, HCoV-NL63, HCoV-HKU1, and HCoV-229E. Incident infections were compared between four population groups: individuals exposed to children, transit users, immunocompromised, and controls. Associations between antibody prevalence indicative of natural infection and demographic variables were assessed using regression analyses. Transit users and those exposed to children were at no greater risk of infection compared to the control group. Fewer infections were detected in the immunocompromised group (p = .03). SARS-CoV-2 seroprevalence was greater in individuals with low income and within ethnic minorities. Our findings suggest that nonpharmaceutical interventions intended to reduce SAR-CoV-2 transmission protected populations at high risk of exposure. The re-emergence of sCoVs and other common respiratory viruses alongside SARS-CoV-2 may alter infection patterns and increase the risk in vulnerable populations.

Increasing the precision of public transit user activity location detection from smart card data analysis via spatial–temporal DBSCAN

Smart Card (SC) systems have been increasingly adopted by public transit (PT) agencies all over the world, which facilitates not only fare collection but also PT service analyses and evaluations. Spatial clustering is one of the most important methods to investigate this big data in terms of activity locations, travel patterns, user behaviours, etc. Besides spatio-temporal analysis of the clusters provide further precision for detection of PT traveller activity locations and durations. This study focuses on investigation and comparison of the effectiveness of two density-based clustering algorithms, DBSCAN, and ST-DBSCAN. The numeric results are obtained using SC data (public bus system) from the metropolitan city of Konya, Turkey, and clustering algorithms are applied to a sample of this smart card data, and activity clusters are detected for the users. The results of the study suggested that ST-DBSCAN constitutes more compact clusters in both time and space for transportation researchers who want to accurately detect passengers’ individual activity regions using SC data.

The effect of psychology factors and environmental concerns on mass rapid transit usage intention among private vehicle users in Klang Valley, Malaysia

Abstract Since its introduction in 2016, Malaysia has made improvements to the mass rapid transit (MRT) system; however, the current year’s usage figures are below expectations and do not meet target levels. Thus, the purpose of this study is to look at the elements that influence the behavioural intentions of private vehicle users to use the MRT in Malaysia. In this study, A total of 403 respondents who are private vehicle users from the Klang Valley were involved and the Pearson correlation test was used to investigate the relationship between the factors that potentially influence the behavioural intention to use MRT among private vehicle users. Next, the factors influencing private car users’ behavioral intention to utilize MRT were also investigated using the multiple linear regression method. The findings of Pearson’s correlation analysis show that all factors namely environmental concerns, perceived behavioural control, attitudes, subjective norms, and behavioural intention have a positive and significant relationship among those factors. Furthermore, the multiple linear regression model demonstrated that drivers of private vehicles are significantly influenced by attitude, subjective norms, and environmental concerns to switch to MRT as a mode of transportation.