Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Provision of metropolitan demand responsive transit and attitude’s role in mode choice
No full textThe present study establishes the concept of demand responsive transit for metropolitan travel (M-DRT) and explores commuters’ mode choice to identify the role of attitudinal characteristics in the Seoul metropolitan area. We develop two discrete choice models (multinomial logit and integrated choice and latent variable models) using a dataset from a web-based stated preference survey. The result shows that unobserved psychological constructs (car-oriented, positive perception on M-DRT, and life satisfaction) play a crucial role in defining mode utility. Specifically, those who are dissatisfied with daily life such as commuting, recreation, and social interaction are likely to prefer M-DRT over traditional alternatives. Also, time valuation (particularly in-vehicle time) for the on-demand mode is significantly lower than those for others, resulting from the productive onboard environment. Based on the findings, we draw insights on the nature of M-DRT, operational strategies, treatment of psychological variables, and the future of the hands-free mode era
Mind the Gap! Gender differences in the predictors of public transport usage intention
No full textPublic transport systems continue to gain ground as a cornerstone of sustainable urban mobility, offering alternatives to private car use, city congestion, and pollution. In this context, the shift toward regular public transport use seems influenced by several factors, with previous studies suggesting that safety concerns, service quality, and environmental value are key predictors of public transport usage intention. However, gender-based differences in travelers’ intentions and choices remain underexplored
Understanding travel patterns of ride-hailing service sub-population groups and effects of transit investment on ride-hailing users’ potential mode switching: A case study of Ho Chi Minh City, Vietnam
No full textRide-hailing services (especially motorcycle-based ride-hailing services -MBRH) have seen a boom in Vietnamese cities because these services can serve as a more efficient alternative to urban mobility. However, relatively little is known about travel patterns of sub-population groups of ride-hailing services, including the spatiotemporal demand, origin, and destination patterns of ride-hailing users, and the effects of transit investment on the mode switching from current ride-hailing users. This information is particularly important for the implementation of traffic management measures focusing on public transport, in light of concerns about the reverse side of the ride-hailing services, such as aggravating the traffic conditions and causing losses in the public transport market. In this paper, we present an in-depth analysis of the travel patterns of MBRH, based on large-scale household survey data collected in Ho Chi Minh City, Vietnam, with the statistical technique of Chi-square and Kruskal-Wallis tests. In addition, the independent and combined effects of Revealed Preference (RP) and Stated Preference (SP) data on the mode switch for MBRH users were studied using the Nested Logit models. The results indicate that speed and flexibility are seen as outstanding features of MBRH in attracting users. Furthermore, mode switch model estimation results show that traditional attributes (i.e., travel time and cost) and transit design factors (i.e., accessibility) are of lower importance to mode-switching behavior compared with sociodemographic factors. These findings suggest that MBRH services fill an important transportation niche and may affect the environment and transportation equity
Electrifying the bus network with trolleybus: Analyzing the in motion charging technology
No full textCurrently, electric buses are becoming more and more popular, and their number in operation is increasing. The range of electric buses is also increasing and solutions that seem to be working almost without fixed infrastructure are being promised. However, this requires the use of high-capacity batteries, which increases the weight and price of the vehicle and causes high costs of battery replacement during operation. Moreover, if we take into account the growing demand for batteries, limited raw material resources, and the environmental impact of the battery production process, the optimization of battery capacity in vehicles may turn out to be a key issue. In this light, trolleybus becomes a sustainable and economically efficient bus electrification technology, if considered in an international scope and a medium- to long-term approach. The article provides a comprehensive study of challenges and potential solutions related to electric buses, which covers the theoretical analysis, technical aspects and practical applications, thus making a valuable resource for readers interested in sustainable urban transport systems. It presents the trolleybus technology, especially with modern solutions, as a sustainable and economically efficient tool for bus electrification. The article shows that the In Motion Charging (IMC) system reduces the need for high-capacity batteries under 100 kWh, which allows to extend their service life up to 15 years and, consequently, to reduce the number of buses needed for operation. The research was based on real measurement data from the transport system in Gdynia (Poland)
The new frontier of urban mobility: a scenario-based analysis of autonomous vehicles adoption in urban transportation
No full textUrban mobility, as a critical component of urban development and sustainability, is on the brink of a major revolution with the advent and widespread adoption of autonomous vehicles (AVs). However, the ambitious vision of a transformative shared economic future and decreased car ownership conflicts with the notion that car dependency will be worsened, leading to significant uncertainties and potential challenges surrounding AVs, particularly in developing countries. In this context, this study contributes to the literature by addressing these uncertainties, through an empirical examination of preferences toward four distinct modes of transportation: “public transport”, “conventional private car”, “autonomous private car”, and “shared autonomous taxi”. To accomplish this, a scenario-based stated preference survey was administered in Istanbul, Turkey, as an example of a megacity in a developing country. The collected data were analyzed using discrete choice models (multinomial and random parameter (mixed) logit models) to reveal the preferences of the survey participants. The findings indicate that considering the social status and significance associated with private vehicle ownership, the challenges of popularizing shared and on-demand AV modes should not be underestimated
Dynamic inference for left behind probabilities on congested metro routes
No full textPassengers left behind is an important measure to describe the degree of congestion in metro systems. Note that passengers’ left behind probabilities are different for their different tap-in times. This paper proposes a methodology for inferring these dynamic probabilities on congested metro routes using automated data. The EM algorithm is used to compute the maximum likelihood estimators of passengers’ dynamic boarding probabilities, and then formulas for estimating dynamic left behind probabilities are presented based on the estimated boarding probabilities. Monte Carlo simulations and a real case application show the effectiveness of the proposed method
Optimization on land development intensity of new industrial towns based on carrying capacity of multi-modal traffic network
No full textIn the context of China’s new urbanization, the establishment of ‘new industrial towns (NITs)’ has emerged as a pivotal strategy for facilitating urban development. However, the current construction of NITs confronts numerous challenges, including issues such as unreasonable land development intensity (LDI) and overburdened transportation infrastructure. Urgent attention and improvement are required to address the coordination gaps between urban land use and transportation systems. This research explores the traffic carrying capacity (TCC) of NITs at a multi-modal super-network level, developing a bi-level programming model for LDI optimization and proposing an improved genetic algorithm (GA) to solve it. Taking the NIT in Laiwu District, Jinan, China as a case study area, the results demonstrate that our approach maximizes the utilization of space–time resources in both bus and car networks, particularly benefiting the latter during morning peak hours, thereby achieving a more balanced supply-demand traffic equilibrium
Data-driven analysis and modeling of individual longitudinal behavior response to fare incentives in public transport
No full textIncentive-based public transport demand management (PTDM) can effectively mitigate overcrowding issues in crowded urban rail systems. Analyzing passengers’ behavioral responses to the incentive can guide the design, implementation, and update of PTDM strategies. Though several studies reported passengers’ responses to fare incentives, they focused on passengers’ short-term behavioral responses. Limited studies explore passengers’ longitudinal behavioral responses for different types of adopters, which is important for policy assessment and adjustment. This paper explores and models passengers’ longitudinal behavior response to a pre-peak fare discount incentive using 18 months of smartcard data in public transport in Hong Kong. We classified adopters into six types based on their temporal travel pattern changes before and after the promotion. The longitudinal analysis reveals that among all adopters, 19% of users change their departure times to take advantage of fare discounts but do not contribute to the goal of reducing peak-hour travel. However, these adopters are more likely to sustain their changed behavior in a long term which is not desired by the incentive program. The spatial analysis shows that the origin station distribution of late adopters is relatively more diverse than the early adopters with more trips starting from distant areas. The diffusion modeling shows that the majority adopters are innovators and the word-of-mouth diffusion effect (imitators) is marginal. The discrete choice model results highlight the heterogeneous impact of factors on different types of adopters and their values of time changes. The significant factors common to adopters are: departure time flexibility, the expected money savings, the required departure time changes, and work locations. The findings are useful for public transport planners and policymakers for informed incentive design and management
Dynamic interlining in bus operations
No full textThe paper introduces and evaluates the concept of the dynamic interlining of buses. Dynamic interlining is an operational strategy for routes with a terminal station at a common hub, allowing a portion of (or all) the fleet to be shared among the routes belonging to the hub (shared fleet) as needed. The shared fleet is dispatched on an on-demand basis to serve scheduled trips on any route to avoid delays and regulate services. The paper examines systematically the impacts of dynamic interlining on service reliability. It formulates the dispatching problem as an optimization problem and uses simulation to evaluate the dynamic interlining strategy under a variety of operating conditions. Using bus routes in Boston’s Massachusetts Bay Transportation Authority (MBTA) as a case study, the strategy’s feasibility and factors that affect its performance are investigated. Results show that dynamic interlining can improve service reliability (increases on-time departures and decreases departure headways variability at the hub). The fraction of the fleet that is shared has the most dominant impact on performance. In the case where all buses are dynamically interlined, the performance improves as route frequency increases and more routes participate in the strategy
Mobility knowledge graph: review and its application in public transport
No full textUnderstanding human mobility in urban areas is crucial for transportation planning, operations, and online control. The availability of large-scale and diverse mobility data (e.g., smart card data, GPS data), provides valuable insights into human mobility patterns. However, organizing and analyzing such data pose significant challenges. Knowledge graph (KG), a graph-based knowledge representation method, has been successfully applied in various domains but has limited applications in urban mobility. This paper aims to address this gap by reviewing existing KG studies, introducing the concept of a mobility knowledge graph (MKG), and proposing a general learning framework to construct MKG from smart card data. The MKG represents hidden travel activities between public transport stations, with stations as nodes and their relations as edges. Two decomposition approaches, rule-based and neural network-based models, are developed to extract MKG relations from smart card data, capturing latent spatiotemporal travel dependencies. The case study is conducted using smart card data from a heavily used urban railway system to validate the effectiveness of MKG in predicting individual trip destinations. The results demonstrate the significance of establishing an MKG database, as it assists in a typical problem of predicting individual trip destinations for public transport systems with only tap-in records. Additionally, the MKG framework offers potential for efficient data management and applications such as individual mobility prediction and personalized travel recommendations