Commuter Experience Research Articles

A Conservation of Resources theory-based framework for studying the commute experience

A Conservation of Resources theory-based framework for studying the commute experience

Development of Models for Ticketing in Public Transportation System in Nigeria

Transportation is a system that humans use to carrying out their day-to-day activities. In the bid to do activities, transportation by any means has to be used. In the public transportation system, they use the manual or traditional method to process ticketing. This causes many flaws such as long queues, communication gap, miscalculation of tickets, improper records of commuters’ information etc. For commuters to gain a convenient platform for bus reservations, real-time information updates, and a smoother experience; and for the companies to benefit from automated reservations, improved communication, and data-driven insights for better decision- making, there is need to develop a model for ticketing in public transportation system. The models for ticketing in urban transportation system have methods that involve pricing algorithm and route algorithm where scaling factors and user capacity planning models are calculated. With the introduction of the ticketing model in transportation system, existing challenges in urban mobility are revolutionized. The intersection of technology and transportation are positioned in such a way that the model seeks to enhance the overall commuter experience seamlessly by integrating reservation processes, real-time updates, amount payable, time and date of travels and communication between branches. The study envisions a shift towards a more accessible, efficient, and user-centred urban transportation system. It represents a transformative catalyst in transportation system especially by land/roads which are being used by automobiles (buses, cars, tricycle etc).

Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion

Accurate passenger flow forecasting is crucial in urban areas with growing transit demand. In this paper, we propose a method that combines advanced machine learning with rigorous time series analysis to improve prediction accuracy by integrating different datasets, providing a prescriptive example for passenger flow prediction in urban rail transit systems. The study employs advanced machine learning algorithms and proposes a novel prediction model that combines two-stage decomposition (seasonal and trend decomposition using LOESS–ensemble empirical mode decomposition (STL-EEMD)) and gated recurrent units. First, the STL decomposition algorithm is applied to break down the preprocessed data into trend terms, periodic terms, and irregular fluctuation terms. Then, the EEMD decomposition algorithm is employed to further decompose the irregular fluctuation terms, yielding multiple IMF components and residual residuals. Subsequently, the decomposed data from STL and EEMD are partitioned into training and test sets and normalized. The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. The synthesis of these sophisticated methodologies serves to substantially enhance both the predictive precision and the broad applicability of the forecasting models. The efficacy of the proposed approach is rigorously evaluated through its application to empirical metro passenger flow datasets from diverse urban centers, demonstrating marked superiority in predictive performance over traditional forecasting methods. The insights gleaned from this study bear significant ramifications for the strategic planning and administration of public transportation infrastructures, potentially leading to more strategic resource allocation and an enhanced commuter experience.

A Novel Spatial–Temporal Deep Learning Method for Metro Flow Prediction Considering External Factors and Periodicity

Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial–temporal relationships inherent in the data and the varying influence of external factors. To model spatial–temporal correlations considering external factors, a novel spatial–temporal deep learning framework is proposed in this study. Firstly, mutual information is utilized to select the highly corrected stations of the examined station. Compared with the traditional correlation calculation methods, mutual information is particularly advantageous for analyzing nonlinear metro flow data. Secondly, metro flow data reflecting the historical trends from different time granularities are incorporated. Additionally, the external factor data that influence the metro flow are also considered. Finally, these multiple sources and dimensions of data are combined and fed into the deep neural network to capture the complex correlations of multi-dimensional data. Sufficient experiments are designed and conducted on the real dataset collected from Xi’an subway to verify the effectiveness of the proposed model. Experimental results are comprehensively analyzed according to the POI information around the subway station.

Prioritizing Subway Station Entrance Attributes to Optimize Passenger Satisfaction in Cold Climate Zones: Integrating Gradient Boosting Decision Trees with Asymmetric Impact-Performance Analysis

Subway station entrances serve as crucial links between urban environments and underground transit systems and are particularly vital in cities with cold climates. Specialized design strategies are essential to address user needs and promote safety and comfort, thereby encouraging sustainable travel in harsh winter conditions. This research utilizes data from Harbin and Shenyang, two winter cities in China, to explore the nonlinear influences of subway entrance attributes on passenger satisfaction through the combined use of gradient-boosting decision trees and asymmetric impact-performance analysis. The findings indicate that most key attributes of subway entrances impact passenger satisfaction asymmetrically, highlighting the significance of their hierarchical importance in generating satisfaction. These attributes are categorized into frustrators, dissatisfiers, hybrids, satisfiers, and delighters, based on their asymmetry levels. Considering the current performance of these attributes, the study identifies priority for improvement at Harbin and Shenyang’s subway entrances. This aids urban designers and city managers in making informed decisions for urban development and enhancing the overall commuter experience in winter cities.

Improving Commute Experience for Private Car Users via Blockchain-Enabled Multitask Learning

Improving Commute Experience for Private Car Users via Blockchain-Enabled Multitask Learning

Pursuing multiple goals during the commute: A dynamic self-regulatory perspective

The current review theorizes that self-regulatory principles can be applied to the commute experience to better understand how spatial navigation and role transition processes interface with each other. Using a multiple goal framework, spatial navigation and role transitions are conceptualized as simultaneous goals, each with their own set of effort allocation processes. Furthermore, the review describes how dynamic and stable features of the commute and of the different roles individuals enact come together to impact spatial navigation and role transition effectiveness, along with well-being and effectiveness in individuals’ “home” and “workplace” roles. The proposed framework offers novel predictions about how and why these two activities impact each other in different ways, providing testable propositions that will help researchers begin to better understand the commute as a time for multiple goal regulation. The review concludes with suggestions for future research aimed at investigating these processes.

Evaluating the Urban Commute Experience: A Time Perception Approach

This article examines the perception of travel time and evaluation of the urban commute experience. It reviews the literature on time perception in psychology, positing perceived travel time as a function of commute characteristics, journey episodes, travel environments, and expectancy. Insights from emerging behavioral economics are drawn to illuminate evaluation of the urban commute experience. The perception–evaluation correspondence presents the potential of a new research approach to travel behavior. A time perception model for evaluating urban commute experience is formulated to accommodate all the posited relationships, with possible moderations by goal attainment, economic values associated, and time urgency. Practical significance of the model is exemplified through its use in explaining mode choice, and as a guide for service planning and design.

Pregnancy and commuting on public transport

IntroductionLimited research has explored the influence of commuting on expectant mother's health and well-being and how expectant mothers can be supported during their commute. The present study aimed to identify the impact of commuting during pregnancy on women's physical and mental health. Further, the effectiveness of Transport for London's baby-on-board badge was explored. MethodThis was a mixed-method study. An online survey of 295 participants over the age of 18 years was conducted to explore their views on commuting and the effectiveness of the baby-on-board badge. A subsample of female participants completed the General Health Questionnaire to assess the impact of commuting while pregnant on women's health. This was followed by a qualitative study in which 15 women who currently or previously commuted to work while pregnant participated in a semi-structured interview about their experience. ResultsCommuting was found to represent a major challenge for some women during pregnancy. It was often a source of stress and anxiety and had detrimental effects on women's health and well-being, especially when they had pre-existing medical conditions. Pregnant women often made adjustments to their commute, such as traveling outside peak times. Although the majority of women commuting into London wore the Baby-on-Board badge during pregnancy, its effectiveness at encouraging commuters to offer their seat or give more space to expectant mothers was mixed. Many women felt the badges were ignored or difficult to see during crowded commutes. ConclusionsThere is limited support for pregnant commuters. The main approach to supporting pregnant commuters is the TfL baby-on-board badge, but the effectiveness of this badge is mixed. A multi-agency approach involving transport agencies, employers, midwives and expectant mothers supporting pregnant commuters may result in a less stressful commute experience and improve women's well-being and productivity at work.

Smartphones for public transport planning and recommendation in developing countries—A review

AbstractIn this era of connected systems that have penetrated everywhere, transport units have become a significant source of data, collected from commuters, vehicles, drivers, or any section being touched by the transport system. These data, which have both spatial as well as temporal aspects, is utilized for a plethora of services like travel assistant systems, multi‐modal transport solutions, real‐time travel information, smart parking, autonomous vehicles, to name a few. With the current buzz of sustainable transport, the use of public transport systems have been popularized owing to the economic and environmental savings. In this review article, we provide a highlight of works, which have tried to utilize techniques to improve multiple sections of the public transport system, primarily focusing on developing economies, thus improving the overall commute experience at various countries.This article is categorized under: Application Areas > Society and Culture Technologies > Internet of Things

Learning from the real practices of users of a smart carpooling app

AimThis paper explores the real practices of users of a smart carpooling application that learns their mobility habits and predicts their future trips to propose relevant matches.MethodA combination of usage data and interviews analysis allows us to explore the commuter experience from registration to the first and the next shared rides.FindingsThe results highlight the shortcomings associated with human factors in carpooling and with human-smart system interactions. They show that perceptions of practical constraints and poor counterparts are the major reasons for difficulty in incorporating carpooling into daily mobility. Psychosocial barriers take different forms at different steps of the carpooling experience (search for information or guarantees about other users, the necessity of conversing with others, much uncertainty about how to behave). The fact that the service is smart amplifies these problems and reduces the desire to carpool again because it creates new misunderstandings (i.e., the user does not understand what the system vs. the other users do) and discomfort in relation to other riders (no answer, too many refusals, necessity of refusing, negative carpool experience, or concern over proposing a bad carpool). Despite these difficulties, the users perceive carpooling as a good solution and a positive human experience when the matching is accurate. We propose some recommendations to overcome the identified difficulties.

Prioritizing Metro Service Quality Attributes to Enhance Commuter Experience: TOPSIS Ranking and Importance Satisfaction Analysis Methods

A metro infrastructure, facility, and service quality investigation based on commuter perception was conducted in this study to explore and prioritize the key attributes influencing overall metro service quality in a typical Indian context. Based on the critical state-of-the-art review, 12 key attributes were identified and they were accommodated in a paper-based questionnaire to elicit commuter perception of importance and satisfaction by using a five-point Likert scale. Subsequently, TOPSIS, an extensively adopted multi-attribute decision-making technique, was carried out to rank the attributes with respect to perceived importance and satisfaction. Then an importance satisfaction analysis (ISA) was conducted to further classify the attributes in four quadrants based on their perceived degree of importance and satisfaction using an ISA matrix. Finally, the derived results from the TOPSIS and ISA analysis were combined and compared to obtain a prioritized set of attributes requiring intervention for better metro service quality in an Indian context. Results of this study clearly indicated the relative strengths and weaknesses of each metro service/infrastructure-specific attribute and presented the probable role of metro authorities for each of them. Attributes such as metro fare, connection to metro, and metro frequency were observed to be the most important, but were not performing satisfactorily, indicating that more emphasis is required on these attributes to improve the overall quality of travel by metro rail in an Indian context. Thus, this methodology would be instrumental in detecting a set of priority areas for improvement in metro rail services, which could contribute to retaining existing commuters and attracting new metro users.

Phase Transition in Taxi Dynamics and Impact of Ridesharing

We develop a numerical model using both artificial and empirical inputs to analyse taxi dynamics in an urban setting. More specifically, we quantify how the supply and demand for taxi services, the underlying road network, and the public acceptance of taxi ridesharing (TRS) affect the optimal number of taxis for a particular city and commuters’ average waiting time and trip time. Results reveal certain universal features of the taxi dynamics with real-time taxi booking: that there is a well-defined transition between the oversaturated phase when demand exceeds supply and the undersaturated phase when supply exceeds demand. The boundary between the two phases gives the optimal number of taxis a city should accommodate, given the specific demand, road network, and commuter habits. Adding or removing taxis may affect commuter experience very differently in the two phases revealed. In the oversaturated phase, the average waiting time is exponentially affected, whereas in the undersaturated phase it is affected sublinearly. We analyse various factors that can shift the phase boundary and show that an increased level of acceptance for TRS universally shifts the phase boundary by reducing the number of taxis needed. We discuss some of the useful insights for the benefits and costs of TRS, especially how, under certain situations, TRS not only economically benefits commuters but can also save the shared parties in overall travel time by significantly reducing the time commuters spend on waiting for taxis. Simulations also suggest that elementary artificial taxi systems can capture most of the universal features of the taxi dynamics. We give detailed methodologies of the microscopic simulations we employed. The relevance of the assumptions and the overall methodology are also illustrated using comprehensive empirical road network and taxi demand in the city-state of Singapore.

Commuting and wellbeing: a critical overview of the literature with implications for policy and future research

ABSTRACTThis review provides a critical overview of what has been learnt about commuting’s impact on subjective wellbeing (SWB). It is structured around a conceptual model which assumes commuting can affect SWB over three time horizons: (i) during the journey; (ii) immediately after the journey; and (iii) over the longer term. Our assessment of the evidence shows that mood is lower during the commute than other daily activities and stress can be induced by congestion, crowding and unpredictability. People who walk or cycle to work are generally more satisfied with their commute than those who travel by car and especially those who use public transport. Satisfaction decreases with duration of commute, regardless of mode used, and increases when travelling with company. After the journey, evidence shows that the commute experience “spills over” into how people feel and perform at work and home. However, a consistent link between commuting and life satisfaction overall has not been established. The evidence suggests that commuters are generally successful in trading off the drawbacks of longer and more arduous commute journeys against the benefits they bring in relation to overall life satisfaction, but further research is required to understand the decision making involved. The evidence review points to six areas that warrant policy action and research: (i) enhancing the commute experience; (ii) increasing commute satisfaction; (iii) reducing the impacts of long duration commutes; (iv) meeting commuter preferences; (v) recognising flexibility and constraints in commuting routines and (vi) accounting for SWB impacts of commuting in policy making and appraisal.

Vanpooling and its effect on commuter stress

Vanpools are used in the United States as a form of commuter transportation, with up to 15 people commuting together in one shared vehicle. This research examined commuter behavior to determine if those who use a vanpool to and from work experience a different level of stress than their single-occupant vehicle counterparts. The outcome of this study is important to refining future studies to include vanpool participants as a standalone category to uniquely consider all specific modes individually. Follow-on research must continue to answer the additional questions of behavior choice to determine if knowledge of lowered stress will prompt users to change said behavior. Within the emerging field of Mobility as a Service, it is useful to understand the differences in commute experience to fully support a holistic mobility framework, accounting for multiple modes that address a specific journey requirement.In this study, performed over the course of two weeks, questionnaire respondents rated their personally-perceived level of stress before and after their commute on a Likert scale of 1 and 10. Overall, the aggregated data revealed that vanpoolers experienced a 21% lower rate of self-reported commuting stress than their single-occupant vehicle counterparts. The difference between commute types was strongest for the after-work-to home portion of the commute. In that instance, vanpool commuters experienced a significant decrease in stress compared to their single-occupant counterparts even though their initial stress prior to the evening commute was higher. This indicates that the commute by vanpool helped them overcome a portion of the stress realized as a result of the experiences of their day.

Commute quality and its implications for commute satisfaction: Exploring the role of mode, location, and other factors

The quality of a worker’s commute significantly impacts her well-being. We seek to add to the nascent literature on this topic by examining how travel mode, location, and other personal characteristics are associated with perceived commute quality and then by exploring implications for commute satisfaction. We use data from the annual Campus Travel Survey of students and employees at the University of California, Davis and focus on three dimensions of the commute experience: how stressful the commute to campus is, whether travel time is seen as wasted time, and the degree to which commuters like their travel mode. Our analysis shows that commute quality differs by residential location, commute mode, student versus employee status, and gender. Overall, bicycle commuters and train commuters report the highest quality commutes, all else equal; bus riders report the lowest quality commutes; the results for those traveling to campus by car are somewhere in the middle. We also find that all three dimensions of perceived commute quality are strongly associated with overall commute satisfaction. These results point to several different strategies for employers like UC Davis that aim to improve commute quality in order to enhance well-being: improve the quality of each mode, encourage a shift to modes offering higher quality commutes, identify “mis-matched” employees who are not using the mode that would yield the highest commute satisfaction, use social marketing techniques to change perceptions of and preferences for commute options, and increase housing near work sites to enable more commuters to live within bicycling distance.

A Human Perspective on the Daily Commute: Costs, Benefits and Trade‐offs

The average worker in Britain spends 139 h/year commuting—the equivalent of 19 standard working days. While the average distance and time taken for journeys to work has been steadily increasing, the average number of journeys has been decreasing at a similar rate. The aggregate picture inevitably masks an array of underlying trends. This paper offers a multi‐perspective examination of commuting drawing upon the literature in transport, planning, geography, economics, psychology, sociology and medicine. It examines statistical evidence on trends in commuting travel behaviour and finds that one in 25 commuters now travels to work in excess of 100 km (both ways) and one in ten commuters now spends over 2 h/day travelling to and from work. It explores the different impacts (economic, health and social) that commuting has on the individuals who conduct it and seeks to understand better the role of commuting for individuals in today’s society. The paper finishes its examination by reviewing the commute experience itself, including attitudes towards it and the use of time during the journey. It concludes by highlighting a dilemma facing transport planning and policy. There are social, economic and financial benefits from an improved travel experience for people with long commute journeys, yet improving the travel experience may itself contribute to the trend towards long‐distance commuting.

Switching From Autopilot

Every commuter knows the feeling. You gaze out the window, recognizing the familiar surroundings, but suddenly it occurs to you that you don't know how you arrived. You are where you think you should be, but you can't recall taking the turns and making the decisions that led you there. Unfortunately, this commuter experience may be all too similar to how we function in other aspects of our lives—including how we practice. Despite an increased focus on patient-specific functional goals, physical therapists (and other health care professionals) lack a general consensus about what constitutes “clinical success.” Like commuters who navigate their way to work unconsciously, we often find ourselves functioning on automatic pilot in the clinic, gravitating toward measurements of impairment or classic measurements of function as indicators of clinical success. For any given patient, however, these types of measurements might not be relevant, particularly when it comes to interventions designed for preventive purposes or to the patient who has a unique lifestyle and values atypical tasks. This month in the Journal, Beattie and colleagues …

Border commuter workers and transfrontier metropolitan structure along the United States‐Mexico Border

This article examines Mexican border commuter workers within the context of the emerging socio-geographical structure of metropolitan areas along the U.S.-Mexico boundary. While border commuters are, in a sense, part of the larger migrant labor pool that travels from Mexico to the United States, this study seeks to demonstrate that the Mexican commuter experience is part of a phenomenon unique to the border region, thus generating a research agenda that moves beyond the framework of general migration theory.