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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 

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.