Monash University, Institute of Transport Studies: World Transit Research (WTR)
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Mode-shift impacts on safety
No full textMany jurisdictions have targets to reduce vehicle travel and crashes. This study examines how these efforts can be integrated. Reducing motor-vehicle-kilometres travelled can reduce crash risk in addition to emissions. However, our current understanding of these impacts is limited, due to the complex interactions between the various risk factors plus inadequate data. This study examined research concerning the effects of mode shifts on casualty crash rates. It found that most risk factors have been studied individually, with many areas nearing academic consensus on relationships. Most studies only considered a few modes and did not explore multiple interactive relationships, and so tend to underestimate the full safety benefits of community-wide shifts from driving to walking, bicycling and public transport. This research has collated recent police crash report and hospital data, and produced a spreadsheet model that enables testing of various mode-shift scenarios. However, more research is needed to evaluate how mode changes are likely to affect crash casualties when other infrastructure and policy factors are taken into account
Dynamic scheduling of flexible bus services with hybrid requests and fairness: Heuristics-guided multi-agent reinforcement learning with imitation learning
No full textFlexible bus is a class of demand-responsive transit that provides door-to-door service. It is gaining popularity now but also encounters many challenges, such as high dynamism, immediacy requirements, and financial sustainability. Scientific literature designs flexible bus services only for reservation demand, overlooking the potential market for immediate demand that can improve ride pooling and financial sustainability. The increasing availability of historical travel demand data provides opportunities for leveraging future demand prediction in optimizing fleet utilization. This study investigates prediction failure risk-aware dynamic scheduling flexible bus services with hybrid requests allowing for both reservation and immediate demand. Equity in request waiting time for immediate demand is emphasized as a key objective. We model this problem as a multi-objective Markov decision process to jointly optimize vehicle routing, timetable, holding control and passenger assignment. To solve this problem, we develop a novel heuristics-guided multi-agent reinforcement learning (MARL) framework entailing three salient features: 1) incorporating the demand forecasting and prediction error correction modules into the MARL framework; 2) combining the benefits of MARL, local search algorithm, and imitation learning (IL) to improve solution quality; 3) incorporating an improved strategy in action selection with time-related information about spatio-temporal relationships between vehicles and passengers to enhance training efficiency. These enhancements are general methodological contributions to the artificial intelligence and operations research communities. Numerical experiments show that our proposed method is comparable to prevailing benchmark methods both with respect to training stability and solution quality. The benefit of demand prediction is significant even when the prediction is imperfect. Our model and algorithm are applied to a real-world case study in Guangzhou, China. Managerial insights are also provided
Assessing the impact of transit accessibility on employment density: A spatial analysis of gravity-based accessibility incorporating job matching, transit service types, and first/last mile modes
No full textTransportation economics studies show that the activity density, in particular, employment density, is influenced by availability and quality of transportation infrastructure and services, including public transit. These studies also show that businesses and economic activities may have unique requirements, preferences, and characteristics, which may lead to varying effect of transportation on different sectors. However, the relationship between transit infrastructure and employment density has been examined mostly using simple transit proximity distance or travel time in the past research and few have used transit accessibility, and none have accounted for first/last mile (FMLM) modality, different public transit service type, and job matching mechanisms. This study attempts to fill these gaps by utilizing a new accessibility measure that is adaptive to the aforesaid features and comparing its relationship with employment density across various industries. The results show a positive and significant relationship between employment density and bus service accessibility for all industrial sectors, while the effect of light rail service is significant only for finance, real estate, insurance, food, and accommodation industrial sectors, and when FMLM modality is driving. Proximity to public transit was found to be a stronger predictor of job density than accessibility. Additionally, the effect of closeness to highway network was almost twice the effect of transit proximity for all sectors, especially for blue-collar jobs. The results also highlight that industrial sectors tend to cluster in areas with higher employment diversity but are indifferent towards higher land use diversity. These results signify several challenges in transportation equity and multimodal planning and policies. Improving regional public transit integration through coordinated physical infrastructure, fare systems, and schedules, along with enhancing walking amenities in key areas, could improve connectivity between activities. Additionally, incorporating equity considerations into land use planning, such as through distributional impact analysis, can help monitor and ensure equity in future urban developments
A systematic review on crowding valuation in public transport
No full textIn public transport, crowding is one of the variables that is likely to influence the decisions of the choice makers. Crowding has become a subject of concern in metropolitan areas, triggering passenger travel behavior, such as shifting from public to private modes of transport, changing routes or departure times, etc. Hence, there is a need to understand the effect of crowding in public transport and its influence on the behavior of travelers. Therefore, this review investigates essential factors (e.g., crowding representation, crowding measurement, modeling framework, etc.) after reviewing the 40 screened studies on the valuation of crowding in public transport. The paper’s findings show that the passenger perception towards crowding is different for varying levels of crowding, modes of transport, study areas, data types, different modeling frameworks, and the underlying distribution of the attribute parameters. A meta-analysis is performed to show the influence of explanatory variables affecting the value of the time multiplier. A net-salary-based city classification is used to make the results transferable. Lastly, this work provides a direction for the selection of the crowding representation, measure, and valuation for future studies. Further, several research gaps are identified for the model formulation, valuation, crowding at different locations, non-linearity, etc
The impacts of COVID-19 on route choice with guidance information in urban rail transit of megacities
No full textThe outbreak of COVID-19 has caused unprecedented decline of ridership in urban rail transit and changed passenger travel habits, which greatly challenges subway operations. Therefore, it is necessary to better understand and quantify the impact of COVID-19 on passenger travel behavior, specifically route choice. Thus, we collected automatic fare collection data and 2060 random samples through a web-based survey in Beijing on passengers’ route choice behavior during the COVID-19 pandemic. This study utilizes an initial dataset to conduct an analysis and introduces an improved Generalized Random Regret Minimization model (GRRM) aimed at understanding passengers\u27 route choice adjustments in response to COVID-19 guidance information. This improved GRRM accounts for two decision-making criteria, namely, maximum utility and minimum regret, and considers passenger heterogeneity. This marks the first instance of capturing the heterogeneity shift effect in route choice perception during the COVID-19 pandemic. The results show that the improved model has the best fitting result with adjusted Rho square of 0.536, demonstrating that the attributes related to guidance information (i.e., information push/time to receive traffic information/perceived route COVID-19 risk) indeed enhance the model’s fit. Furthermore, the research employs Value of Information Time to quantify the preference of passengers for information in various groups. Compared with the usual scenario, women, young and non-commuter passengers are more likely to receive an early information update to plan their trips in advance. Finally, the perceived risk of COVID-19 on routes is examined in relation to passengers’ personal attributes. It is observed that the elderly and students exhibit heightened sensitivity to the epidemic at all stages, while young passengers and commuters are particularly sensitive only during the small-scale epidemic. These findings offer valuable insights for managers to implement targeted strategies, thereby enhancing passenger flow control and encouraging increased subway ridership
Travel behavior and system dynamics in a simple gamified automated multimodal network
No full textAutomated Vehicles (AVs) are poised to disrupt travel patterns and the sustainability of transportation networks. Conventional methods for studying these changes, such as stated preference surveys and agent-based simulations, have limitations. Serious games offer a promising alternative, providing a controlled and engaging environment for investigating travel behavior. In our study, 200 participants, grouped into sessions of 10, engaged in a competitive serious game simulating 50 daily choices of travel mode and departure time across three automated options. Two scenarios were examined: one with recurring congestion and another with nonrecurring congestion. Automated transit had fixed schedules, while private and shared rides could adapt to a congested bottleneck. Results revealed that ridesharing dominated, reaching 60% mode share under recurring congestion, displacing transit, and a comparative equilibrium emerged between shared and private rides. In the nonrecurring congestion scenario, ridesharing dropped to 37%, and a comparable multimodal equilibrium developed. Participants rarely achieved the optimal score, attaining a maximum of 88% of its potential. This study highlights a policy paradox: unregulated AV traffic can reduce transit use, exacerbate recurring congestion, yet necessitate increased transit investment to address nonrecurring congestion, confirming the Downs-Thomson paradox. Creating appealing mass transit alternatives is imperative to ensure efficiency and sustainability in the era of automated mobility
Simulation of land use changes by capturing the different impacts of rail transit in both mother city and new towns
No full textRail transit system plays an important role in connecting the urban center, new towns and suburbs in the metropolitan area. Exploring the methods of integrating rail transit and land use in different locations is essential to improve trip rates and promote transit-oriented development (TOD). Taking Wuhan, China, as a case study, this research uses a cellular automaton-based random forest (CA-RF) model to simulate land use change surrounding rail transit by capturing the impact differences in mother city (MC) and new towns (NT). An impact area identification model is proposed to recognize the most sensitive threshold of land use change in both MC and NT stations, which are 1000m and 1200m, respectively. The characteristics of land use change from 2010 to 2020 are analyzed and compared. The CA-RF model calibration shows that transportation and spatial accessibility present wide variations in driving land use change surrounding the MC and NT stations. The high accuracy of CA-RF model, which is 88.1% for MC and 82.1% for NT, indicating its effectiveness in quantifying the non-linear relationship of land use change and spatial attributes. The forecasted land use maps for proposed rail stations provide a reference for governments and planners in policy intervention and decision-making. This research framework can be applied to other metropolitans to explore the regulations of land use development and promote transit-oriented sustainable development
Missing Typical Weekdays in Travel Surveys: A Pseudo-Panel Approach to Explore Weekly Travel Patterns
No full textTravel surveys generally rely on single-day travel diaries where respondents report their travel information for a typical weekday. However, the concept of a typical weekday does not represent the current reality, as travel behavior has been largely altered in the post-pandemic period. Besides, the conclusions based on analyzing single-day travel diaries lack the ability to capture daily variations in travel behavior. In response to these concerns, this research proposed a framework to expand single-day travel diaries into longitudinal multi-day travel data using a pseudo-panel approach. Leveraging the constructed longitudinal data, the study evaluated the determinants of people’s daily participation in work–school, routine, and discretionary activities. Fixed and random effects panel data estimation models were used for this purpose. Results showed that activity participation is largely attributed to vehicle ownership, income, education, driving license, and household structure. Noticeable daily trends were observed in work–school and discretionary activities. A negative association between transit pass ownership and activity participation was noticed, suggesting social exclusion faced by transit users. In addition, teleworkers were found to be relatively more engaged in discretionary activities. Suburban residents were found to travel longer to participate in activities compared to urban dwellers. The proposed research framework can support future activity-based modeling aspects, such as activity participation, scheduling, mode choice, shared travel, and destination choice models, specifically addressing the “typical weekday” barrier
Exhaust emissions and energy conversion of hybrid and conventional CNG buses
No full textThe real-driving emission (RDE) test was employed involving a conventional compressed natural gas (CNG) bus and its hybrid counterpart powered by the same engine type along the same route. By calculating the energy balance of engine and vehicle, hybrid bus saves energy in three ways: engine efficiency increase (30 %), regenerative braking (21 %) and without idling (7 %). But the higher curb weight offset 32 % of the benefits, resulting in 24 % improvement in comprehensive efficiency. The NOx and particulate number (PN) emissions of the hybrid bus could be reduced by 46 % and 39 %, respectively. The emission causes of all pollutants were further classified into: cold-start, restart and hot running. Restart accounts for 52 % of CO emission and hot running for 55 % of NOx emission. Frequent engine restart and short engine running durations (\u3c50 s) impair efficiency and emissions. The findings can help policymakers assess the environmental impact of transport electrification
Spatiotemporal patterns and factors influencing metro ridership of people with disabilities
No full textRail transit’s safety and convenience have made it a preferred option for people with disabilities. In this study, utilizing the geographically weighted regression based on the network weight matrix (NWM GWR) model, we investigated the spatiotemporal patterns and influencing factors of metro ridership among this group in Nanjing, China. Our findings revealed significant fluctuations in metro ridership across seasons, with a decrease observed during summer. We also discovered that people with disabilities had evening peak hours one hour earlier than regular peak hours on weekdays, while weekends did not exhibit a significant peak. Geographically, metro trips of individuals with disabilities were concentrated in Old City and Main City. Furthermore, the results revealed that except distance to CBD and access to barrier-free facilities, the other factors positively influenced weekday and weekend ridership of people with disabilities. These insights provide valuable guidance for enhancing the mobility and accessibility of people with disabilities