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Current free and subscription-based trip planners have heavily focused on providing available transit options to improve the first and last-mile connectivity to the destination. However, those trip planners may not truly be multimodal to vulnerable road users (VRU)s since those selected side walk routes may not be accessible or feasible for people with disability. Depending on the level of availability of digital twin of travelers behaviors and sidewalk inventory, providing the personalized suggestion about the sidewalk with route features coupled with transit service reliability could be useful and happier transit riders may boost public transit demand/funding and reduce rush hour congestion. In this paper, the adaptive trip planner considers the real-time impact of environment changes on pedestrian route choice preferences (e.g., fatigue, weather conditions, unexpected construction, road congestion) and tolerance level in response to transit service uncertainty. Side walk inventory is integrated in directed hypergraph on the General Transit Feed Specification to specify traveler utilities as weights on the hyperedge. A realistic assessment of the effect of the user-defined preferences on a traveler’s path choice is presented for a section of the Boston transit network, with schedule data from the Massachusetts Bay Transportation Authority. Different maximum utility values are presented as a function of varying traveler’s risk-tolerance levels. In response to unprecedented climate change, poverty, and inflation, this new trip planner can be adopted by state agencies to boost their existing public transit demand without extra efforts 

Paratransit services are indispensable for vulnerable road users, especially for the elderly and the disabled who lack other available mobility options or face lower accessibility to public transit systems. There are some recurrent disturbances that would be simpler to predict and, it is reasonable suspicion that there exists a significant relationship between the spatiotemporal characteristics of a location and the amount of potential delay. Therefore, this study proposes the incorporation of dwell time uncertainty in paratransit operation systems. It will use temporal multimodal multivariate learning (TMML) and the contextual bandit (CB) to estimate the impact of features on loading time. 

Estimating multimodal distributions of travel times (TT) from real-world data is critical for understanding and managing congestion. Mixture models can estimate the overall distribution when distinct peaks exist in the probability density function, but no transfer of mixture information under epistemic uncertainty across different spatiotemporal scales has been considered for capturing unobserved heterogeneity. In this paper, a physics-informed and -regularized (PIR) prediction model is developed that shares observations across similarly distributed network segments over time and space. By grouping similar mixture models, the model uses a particular sample distribution at distant non-contiguous unexplored locations and improves TT prediction. The model includes hierarchical Kalman filtering (KF) updates using the traffic fundamental diagram to regulate any spurious correlation and estimates the mixture of TT distributions from observations at the current location and time sampled from the multimodal and multivariate TT distributions at other locations and times. In order to overcome the limitations of KF, this study developed dynamic graph neural network (GCN) model which uses time evolving spatial correlations. The KF model with PIR predicts traffic state with 19% more accuracy than TMML model in Park et al.(2022) and GCN model will further reduce the uncertainty in prediction. This study uses information gain from explored correlated links to obtain accurate predictions for unexplored ones. 

Drivers traveling on the road usually choose the route which will reduce their own travel time without giving a thought about how this decision will affect other users in the traffic network. Their behaviours leads to problem of oscillating congestion on the roads in the event of traffic disruption. This paper addresses this issue by adopting a competing optimal approach for informed and uninformed drivers. Informed drivers are proposed with alternate routes that reduce the system cost while uninformed drivers continue their journey on originally proposed routes. This strategy of dispersing traffic can reduce congestion significantly. The framework is implemented using Transmodeler, a traffic simulation by experimenting with varying percentage of informed drivers in the network. 

Estimating multimodal distributions of travel times from real-world data is critical for understanding and managing congestion. Mixture models can estimate the overall distribution when distinct peaks exist in the probability density function, but no transfer of mixture information under epistemic uncertainty across different spatiotemporal scales has been considered for capturing unobserved heterogeneity. In this paper, a physics-informed and -regularized prediction model is developed that shares observations across similarly distributed network segments across time and space. By grouping similar mixture models, the model uses a particular sample distribution at distant non-contiguous unexplored locations and improves TT prediction. Compared to traditional prediction without those updates, the proposed model's 19% of performance show the benefit of indirect learning. Different from traditional travel time prediction tools, the developed model can be used by traffic and planning agencies in knowing how far back in history and what sample size of historic data would be useful for current prediction. 

With Mobility-as-a-Service platforms moving toward vertical service expansion, we propose a destination recommender system for Mobility-on-Demand (MOD) services that explicitly considers dynamic vehicle routing constraints as a form of a ``physical internet search engine''. It incorporates a routing algorithm to build vehicle routes and an upper confidence bound based algorithm for a generalized linear contextual bandit algorithm to identify alternatives which are acceptable to passengers. As a contextual bandit algorithm, the added context from the routing subproblem makes it unclear how effective learning is under such circumstances. We propose a new simulation experimental framework to evaluate the impact of adding the routing constraints to the destination recommender algorithm. The proposed algorithm is first tested on a 7 by 7 grid network and performs better than benchmarks that include random alternatives, selecting the highest rating, or selecting the destination with the smallest vehicle routing cost increase. The RecoMOD algorithm also reduces average increases in vehicle travel costs compared to using random or highest rating recommendation. Its application to Manhattan dataset with ratings for 1,012 destinations reveals that a higher customer arrival rate and faster vehicle speeds lead to better acceptance rates. While these two results sound contradictory, they provide important managerial insights for MOD operators. 

While public transit network design has a wide literature, the study of line planning and route generation under uncertainty is not so well covered. Such uncertainty is present in planning for emerging transit technologies or operating models in which demand data is largely unavailable to make predictions on. In such circumstances, this paper proposes a sequential route generation process in which an operator periodically expands the route set and receives ridership feedback. Using this sensor loop, a reinforcement learning-based route generation methodology is proposed to support line planning for emerging technologies. The method makes use of contextual bandit problems to explore different routes to invest in while optimizing the operating cost or demand served. Two experiments are conducted. They (1) prove that the algorithm is better than random choice; and (2) show good performance with a gap of 3.7% relative to a heuristic solution to an oracle policy.