Monash University, Institute of Transport Studies: World Transit Research (WTR)
Not a member yet
11112 research outputs found
Sort by
The indirect effect of travel mode use on subjective well-being through out-of-home activities
No full textThe issue of the effects of travel on subjective well-being (SWB) has recently attracted increasing interest in transport studies. A common finding is that travel affects SWB indirectly through out-of-home activities. However, little is known about how to operationalize this relationship. In this study, we proposed a conceptual model and estimated structural equation models relating travel mode use and activities with multiple SWB dimensions, including affective components (positive affect and negative affect) and cognitive components (belongingness, achievement, and confidence in coping). We used data from a national mobility project in Japan (N = 13,000) to estimate the postulated models. We found that while public transport use enhanced the cognitive components of SWB, it also had a negative effect on the affective components of SWB. Car use affected SWB in a more complex pattern; it promoted SWB by enabling leisure activities but also reduced SWB dimensions of belongingness and achievement through shopping activities. Active travel modes did not have a clear effect on SWB; for example, walking to school was associated with increased belongingness, whereas walking for shopping negatively influenced belongingness. Other contributing factors, such as COVID-19 worry, car access, and the ‘going-out’ problem-solving style, were also found to influence multiple dimensions of SWB. Overall, our study showed how the effects of activities on multiple dimensions of SWB varied with different travel modes, thereby revealing the indirect effect of travel mode use on SWB via activities. Suggestions for shaping transport policies towards SWB are also discussed
Using system dynamics to understand long-term impact of new mobility services and sustainable mobility policies: an analysis pre- and post-COVID-19 pandemic in Rio de Janeiro, Brazil
No full textSustainable transport policies are fundamental to adapt transport capacity to existing and future travel demand. In this context, this study aims to develop a System Dynamics (SD), to verify the effects of these policies, focusing on congestion and air pollution. Combined with the SD, the discrete choice utility approach was used to predict the modal share in different policy spaces. In addition, it is carried out a case study in Rio de Janeiro in two realities: pre- and post-pandemic. The results show how mitigation policies can reduce transport externalities (congestion and pollution). The encouragement of high-capacity public transport and car ownership control are the best measures, obtaining high simulation scores. The post-pandemic scenario shows that reducing travel demand is the key to achieving better results. All scores obtained in this scenario are better than in pre-pandemic scenario. Finally, results point out that ride-hailing should be used in a conscious way
Pre-peak fare discount policy for managing morning peak demand of interregional bus travel: a case study in Seoul metropolitan area
No full textPre-peak fare discount policies have become increasingly common in urban transit services and show potential to spread morning peak-hour demand. This study analyzes the departure time choice behavior of interregional bus passengers during morning hours. A mixed logit model is applied to estimate the probability of shifting departure time as a function of fare discount rate, in-vehicle congestion, and other influential factors, accommodating heterogeneity of passenger preferences. The analysis data are obtained through a stated preference survey, which is conducted to passengers who regularly use the interregional bus in Seoul metropolitan area. Choice models are segmented by departure time periods (peak, pre-peak, and post-peak) and occupation types (fixed time worker, flexible time worker, and student). Marginal utilities of the unobserved preferences are estimated to suggest policy implications. From the experimental analysis, target passengers for shifting departure time during peak hour are suggested as flexible time workers and students
Assessing shared auto-rickshaws adoption by intra-city commuters as part of the public transport system: The influence of negative encounters on passenger satisfaction
No full textEmerging research has highlighted the significant role auto-rickshaws play in travel services, and yet, compared to other transport modes, they have received relatively less attention in research. No study has investigated travel behaviour and negative encounters in shared auto-rickshaws. The assumption that negative encounters deter users from utilising shared auto-rickshaws has not yet been validated. Moreover, previous studies have focused on adopting auto-rickshaws and travel behaviour, neglecting the specific dynamics of shared services within the public transport landscape. Here, we investigate the determinants influencing shared auto-rickshaw usage as a distinct mode in Ghana. We also identify negative attributes by drawing the link between negative user encounters and their impact on satisfaction, loyalty, and openness to policy propositions. The study deployed structural equation modelling, which is generally preferred in transport research. We discovered that ‘Availability’ is the most significant factor in predicting the adoption of shared auto-rickshaws. The estimated model established that negative encounters substantially impact users’ satisfaction, and users’ satisfaction was impacted by discomfort with seating arrangement, driver conduct, fleecing of users, and long travel time, among others. Older commuters reported higher levels of satisfaction than younger commuters, whereas commuters with higher levels of education tend to report lower levels of satisfaction. This research expands the limited understanding of shared auto-rickshaw usage by advancing our theoretical grasp of how negative encounters influence satisfaction and loyalty from a user-centric perspective. These insights can aid policymakers, service providers, and transport authorities in crafting legislation that prioritises user needs, enhances service quality, and creates a positive commuting experience. Furthermore, this study discusses managerial implications for transport authorities and policymakers
Promoting a sustainable behavioral shift in commuting choices: the role of previous intention and “personalized travel plan” feedback
No full textAccording to the European Environment Agency (European Environmental Agency, EEA, 2018), road transport is responsible for 72% of all transport-related greenhouse gas emissions in the European Union (EU), which accounts for 25% of total energy-related emissions (Eurostat, 2018). Thus, it is crucial to identify drivers and barriers to more sustainable transport behaviors. In this regard, the Norm Activation Model and Theory of Planned Behavior have often been used as conceptual frameworks for predicting such behaviors. The present study aimed to analyze the differential impact of both socio-psychological factors and persuasive messages sent through a Personalized Travel Plan (PTP) on Sustainable Transport Choices (STC). To reach this aim we administered a survey two times (T1: Oct./Dec. 2020; T2: March/May 2021) to 398 car users. Measures of constructs included in the Norm Activation Model and the Theory of Planned Behavior, such as behavioral intention, attitude, perceived behavioral control, beliefs, and personal and social norms, were detected. Participants were then exposed to a PTP built on feedback information regarding kilocalories, CO2 emissions, cost, and time savings when using sustainable transport compared to driving a car. Structural Equation Modeling (SEM) analysis shows that intention to use sustainable transport in T1 is on one side directly predicted by personal norm, perceived behavioral control, and attitude, and on the other side emerged as the main predictor of sustainable travel choices in T2, together with kcal spent, whereas time was the major barrier. Implications and future developments are discussed in the light of the conceptual framework
Agent-based decision-support model for bus route redesign in networks of small cities and towns: case study of Agder, Norway
No full textSmall cities and towns often struggle to provide high-quality public transport services to daily commuters. This is reflected in the modal split, where the share of car users dominates. Such a problem requires a modern solution, where transport planners can verify the impact of potential transport network improvements on the travel behavior of the residents before the changes are actually deployed. This study aims to demonstrate the usefulness of employing an agent-based simulation tool in the decision process for redesigning an express service regional bus route connecting a network of small cities and towns. The model was initially developed as a Mobility as a Service simulation solution for suburban areas of European metropolises. The model is adapted and applied to a case study for the region of Agder, Norway, simulating the impact of nine different scenarios on the patronage of a specific bus route. The simulation model proposes to upgrade the classic agent structure to a persona profile designed specifically for the case study. The main objective of this research is to identify the scenario that maximizes patronage while minimizing total route travel time and additional costs. The results suggest that the proposed model can be successfully adapted from suburban metropolitan areas to the realities of the considered case study, and potentially other similar regions. Specifically, out of the nine proposed scenarios, the model identified four promising ones. One of the four scenarios also fits the cost constraints imposed by the transport provider. The model provides a solid approach for analyzing complex transport systems that are practically impossible to consider in detail if the analysis is done without computer support. Thus, the results can be used as a decision support system for public transport planning and operations in networks of small cities and towns
Revenue sharing and resource allocation for cooperative multimodal transport systems
No full textThe past decade has witnessed a significant growth in the diversity of transport services. The cooperation among different transport service providers (TSPs) helps to reduce operational costs and improve resource utilization rates by pooling the mobility resources together and facilitating the centralized optimal resource allocation. However, TSPs are selfish entities who seek to maximize their own profits. They will form a coalition only when cooperation brings them more benefits. Hence, to ensure the stability of such a coalition, the key challenge lies in designing revenue-sharing schemes that make TSPs better off and the coalition reaches Pareto efficiency, while accounting for individual TSPs’ decentralized resource allocation decisions. In this study, we propose a two-stage hierarchical game theoretical model to design an efficient revenue-sharing rule that can stabilize the coalition. The first stage solves the revenue-sharing problem in a cooperative game, while the second stage solves TSPs’ resource allocation strategies in a decentralized non-cooperative game. We analytically prove the stability and efficiency of the revenue-sharing scheme and derive managerial insights through numerical experiments
Evaluation and determinants of metro users\u27 regularity: Insights from transit one-card data
No full textRegularity is typically defined based on the repetitive travel behavior of individuals, referring to how often travelers would utilize a specific service within a given spatio-temporal context. However, previous research on metro users\u27 regularity primarily utilized basic metric, for example metro trip frequency, to measure regularity. What\u27s more, metro smart card data typically encompasses time, spatial features, and card type information, lacking individual attributes such as age, gender, and type of residence, which limits in-depth analysis correlating individual attributes with travel behavior. The study obtained transit one-card data from Nanjing, China, which enabled us to extract metro user\u27s travel and individual information. Thus, the entropy rate methodology was employed to measure metro users\u27 regularity, while machine learning techniques were used to analyze non-linear effects of built environment, travel-related, and individual attributes on regularity. Results indicate that the built environment, travel-related, and individual attributes account for 66.66%, 33.31%, and 0.03% of the total relative importance, respectively. Two most influential variables impacting regularity, namely entertainment POIs at the origin level (17.77%) and weekdays (17.51%), belong to the built environment and travel-related attributes, respectively. In terms of individual attributes, age exhibits a greater impact on regularity compared to gender and type of residence, manifested in the variation of regularity among different age groups. This finding can assist metro policymakers in understanding metro users\u27 travel behavior, aiming to enhance operational efficiency and optimize the user experience
Data-Driven Real-Time Denied Boarding Prediction in Urban Railway Systems
No full textProviding real-time crowding information in urban railways would enable informed travel decisions and encourage cooperative behavior of passengers, as well as improve operating efficiency and safety. However, the problem of real-time crowding prediction is not trivial because of the unavailability of ground-truth crowding data, particularly for the direct impact of crowding on passengers (e.g., denied boarding on platforms). This paper proposes a data-driven method for real-time denied boarding prediction in urban railway systems using automated fare collection (AFC) and automated vehicle location (AVL) data. It predicts the denied boarding probability distribution or its derived metrics as a function of explanatory variables, including demand, operations, and incident-related factors. The method is validated through a case study covering 18 months on Hong Kong Mass Transit Railways. The results highlight the model’s accurate and robust performance in predicting denied boarding on platforms using purely AFC and AVL data (e.g., an average of 6%–7% error) under both recurrent and non-recurrent situations, in which transfer demand-related factors contribute most to the prediction. The prediction model outputs can support proactive operations control and management, as well as provision of customer information without relying on expensive and time-consuming data collection efforts. Customer information, for example, can include the average number of trains to wait before boarding or the average waiting time on the platform
Catchment-Area Delineation Approach Considering Travel Purposes for Station-Level Ridership Prediction Task
No full textStation-catchment-area delineation is a key component of direct ridership models for urban rail-transport systems as it can determine the relationship between the urban-rail-transit station-level ridership and the variables within the station catchment area. The neglect of differences in the passenger-flow distribution for different travel purposes in previous studies has led to low accuracy of the obtained walk-to-station distances. Therefore, this paper proposes a station-catchment-area delineation method which is based on web map data to obtain accurate walk-to-station distances and considers differences in the distance thresholds and the ridership attraction intensity (RAI) for six travel purposes (corresponding to commercial, medical, residential, educational, administrative, and recreational land uses). In the case study, the ridership data of Xi’an Metro, the 2015 Xi’an Residential Travel Survey data, and the corresponding Gaode Map data are employed to extract passengers’ walking-distance distribution for several travel purposes to delineate the station catchment areas and build direct ridership models. Several geographically weighted regression (GWR) models are constructed to evaluate and examine the effects of the various station-catchment-area delineation methods on the model findings. The obtained results show that the proposed station-catchment-area delineation method significantly improves the ridership prediction performance compared with the traditional circular-buffer method, with the entry and exit ridership prediction accuracy improving by 3.57% and 6.65% on average, respectively. Finally, this study will guide transportation planners on how to delineate station catchment areas when constructing direct-demand models for urban rail stations