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1. Crime-Avoiding Routing Navigation

   https://doi.org/10.58245/ipsi.tir.2401.06  

   Rishe, Naphtali; Sadjadi, Masoud; Adjouadi, Malek (January 2024, IPSI Transactions on Internet Research)

   Extensive prior work has provided methods for the optimization of routing based on the criteria of travel time and/or the cost of travel and/or the distance traveled. A typical method of routing involves building a graph comprised of street segments, assigning a normalized weighted value to each segment, and then applying the weighted-shorted path algorithm to the graph to find the best route. Some users desire that the routing suggestion include consideration pertaining to the reduction of risk of encountering violent crime. For example, a user desires a leisurely walk via a safe route from her hotel in an unknown city. Here, we present a method to quantify such user preferences and the risks of encountering crime and to augment the standard routing methods by assigning weights to safety considerations. The proposed method’s advantages, in comparison to other crimeavoidance routing algorithms, include weighting crime types with respect to their potential detrimental value to the user, with temporal qualification and quantification of crime and its statistical aggregation at the geographic resolution down to a city block. 

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2. Trajectory-aware Lowest-cost Path Selection: A Summary of Results

   https://doi.org/10.1145/3340964.3340971  

   Li, Yan; Kotwal, Pratik; Wang, Pengyue; Shekhar, Shashi; Northrop, William (August 2019, SSTD '19: Proceedings of the 16th International Symposium on Spatial and Temporal Databases)

   The trajectory-aware lowest-cost path selection problem aims to find the lowest-cost path using trajectory data. Trajectory data is valuable since it carries information about travel cost along paths, and also reflects travelers' routing preference. Path-centric travel cost estimation models using trajectory data grows popular recently, which considers the auto-correlation of the energy consumption on different segments of a path. However, path-centric models are more computationally expensive than edge-centric models. The main challenge of this problem is that the travel cost of every candidate path explored during the process of searching for the lowest-cost path need to be estimated, resulting in high computational cost. The current path selection algorithms that use path-centric cost estimation models still follow the pattern of "path + edge" when exploring candidate paths, which may result in redundant computation. We introduce a trajectory-aware graph model in which each node is a maximal trajectory-aware path. Two nodes in the trajectory-aware graph are linked by an edge if their union forms a trajectory-union path. We then propose a path selection algorithm to find a path in the proposed trajectory-aware graph which corresponds to the lowest-cost path in the input spatial network. We prove theoretically the proposed algorithm is correct and complete. Moreover, we prove theoretically that the proposed path selection algorithm cost much less computational time than the algorithm used in the related work, and validate it through experiments using real-world trajectory data. 

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3. Adaptive personalized routing for vulnerable road users

   https://doi.org/10.1049/itr2.12191  

   Darko, Justice; Folsom, Larkin; Pugh, Nigel; Park, Hyoshin; Shirzad, Khadijeh; Owens, Justin; Miller, Andrew (April 2022, IET Intelligent Transport Systems)

   Abstract This research presents an adaptive and personalized routing model that enables individuals with mobility impairments to save their route preferences to a mobility assistant platform. The proactive approach based on anticipated user need accommodates vulnerable road users' personalized optimum dynamic routing rather than a reactive approach passively awaiting input. Most currently available trip planners target the general public's use of simpler route options prioritized based on static road characteristics. These static normative approaches are only satisfactory when conditions of intermediate intersections in the network are consistent, a constant rate of change occurs per each change of the segment condition, and the same fixed routes are valid every day regardless of the user preference. In this study, the vulnerable road user mobility problem is modeled by accommodating personalized preferences changing by time, sidewalk segment traversability, and the interaction between sidewalk factors and weather conditions for each segment contributing to a path choice. The developed reinforcement learning solution presents a lower average cost of personalized, accessible, and optimal path choices in various trip scenarios and superior to traditional shortest path algorithms (e.g., Dijkstra) with static and dynamic extensions. 

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4. Turn Constrained Shortest Path

   https://doi.org/10.1109/HONET59747.2023.10374669  

   Allani, Amogh; Yang, KwangSoo (December 2023, International Conference on Smart Communities: Improving Quality of Life using AI, Robotics and IoT (HONET))

   Given a transportation network, a source node s, a destination node t , and the number of maximum possible turnings b , the Turn-Constrained Shortest Path (TCSP) problem is to find the route that minimizes the travel distance and meets the turn-constraint. The TCSP problem is important for societal applications such as shipping and logistics, emergency route planning, and traffic management services. We propose novel approaches for TCSP to meet the turn-constraint while minimizing the travel distance for the vehicle route. Experiments using real-world datasets demonstrated that the proposed algorithms can minimize the travel distance and meet the turn-constraint; furthermore, it has comparable solution quality to the unconstrained shortest path and significantly reduces the computational cost. 

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5. Resilience of Urban Street Network Configurations under Low Demands

   https://doi.org/10.1177/0361198120933269  

   Yu, Zhengyao; Gayah, Vikash V. (September 2020, Transportation Research Record: Journal of the Transportation Research Board)

   null (Ed.)

   Urban street networks are subject to a variety of random disruptions. The impact of movement restrictions (e.g., one-way or left-turn restrictions) on the ability of a network to overcome these disruptions—that is, its resilience—has not been thoroughly studied. To address this gap, this paper investigates the resilience of one-way and two-way square grid street networks with and without left turns under light traffic conditions. Networks are studied using a simplified routing algorithm that can be examined analytically and a microsimulation that describes detailed vehicle dynamics. In the simplified method, routing choices are enumerated for all possible origin–destination (OD) combinations to identify how the removal of a link affects operations, both when knowledge of the disruption is and is not available at the vehicle’s origin. Disruptions on two-way networks that allow left turns tend to have little impact on travel distances because of the availability of multiple shortest paths between OD pairs and the flexibility in route modification. Two-way networks that restrict left turns at intersections only have a single shortest-distance path between any OD pair and thus experience larger increases in travel distance, even when the disruption is known ahead of time. One-way networks sometimes have multiple shortest-distance routes and thus travel distances increase less than two-way network without left turns when links are disrupted. These results reveal a clear tradeoff between improved efficiency and reduced resilience for networks that have movement restrictions, and can be used as a basis to study network resilience under more congested scenarios and in more realistic network structures. 

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