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1. Scenic routing navigation using property valuation

   https://doi.org/10.1186/s40537-023-00736-1  

   Rishe, Naphtali; Amini, M. Hadi; Adjouadi, Malek (May 2023, Journal of Big Data)

   Abstract Extensive prior work has provided methods for the optimization of routing based on weights assigned to travel duration, and/or travel cost, and/or the distance traveled. Routing can be in various modalities, such as by car, on foot, by bicycle, via public transit, or by boat. 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 in order to find the best route. Some users desire that the routing suggestion include consideration pertaining to the scenic-architectural quality of the path. For example, a user may seek a leisure walk via what they might deem as visually attractive architecture. Here, we are proposing a method to quantify such user preferences and scenic quality and to augment the standard routing methods by giving weight to the scenic quality. That is, instead of suggesting merely the time and cost-optimal route, we will find the best route that is tailored towards the user’s scenic quality preferences as an additional criterion to the time and cost. The proposed method uniquely weighs the scenic interest or residential street segments based on the property valuation data. 

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2. Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic

   Wollenstein-Betech, Salomón; Houshmand, Arian; Salazar, Mauro; Pavone, Marco; Cassandras, Christos G.; Paschalidis, Ioannis Ch (September 2020, IEEE International Conference on Intelligent Transportation Systems)

   null (Ed.)

   This paper studies congestion-aware route- planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on- demand mobility under mixed traffic conditions. Specifically, we first devise a network flow model to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture reactive exogenous traffic consisting of private vehicles selfishly adapting to the AMoD flows in a user- centric fashion by leveraging an iterative approach. Finally, we showcase the effectiveness of our framework with a case- study considering the transportation sub-network in New York City. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, whilst the combination of AMoD with walking or micromobility options can significantly improve the overall system performance. 

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3. Tradeoffs between safety and time: A routing view

   https://doi.org/10.1016/j.trc.2019.09.020  

   Carmody, Daniel R.; Sowers, Richard B. (November 2019, Transportation research Part C Emerging technologies)

   This article proposes a data-driven combination of travel times, distance, and collision counts in urban mobility datasets, with the goal of quantifying how intertwined traffic accidents are in the road network of a city. We devise a bi-attribute routing problem to capture the tradeoff between travel time and accidents. We apply this to a dataset from New York City. By visualizing the results of this computation in a normalized way, we provide a comparative tool for studies of urban traffic. 

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4. A Harmonic Extension Approach to Collaborative Ranking

   Kuang, Da; Shi, Zuoqiang; Osher, Stanley; Bertozzi, Andrea L (December 2017, International Symposium on Nonlinear Theory and its Applications)

   We present a new perspective on graph based methods for collaborative ranking for recommender systems. Unlike user-based or item-based methods that compute a weighted average of ratings given by the nearest neighbors, or low-rank approximation methods using convex optimization and the nuclear norm, we formulate matrix completion as a series of semi-supervised learning problems, and propagate the known ratings to the missing ones on the user-user or item-item graph globally. The semi-supervised learning problems are expressed as Laplace-Beltrami equations on a manifold, or namely, harmonic extension, and can be discretized by a point integral method. Our approach, named LDM (low dimensional manifold), does not impose a low-rank Euclidean subspace on the data points, but instead minimizes the dimension of the underlying manifold. It turns out to be particularly effective in generating rankings of items, showing decent computational efficiency and robust ranking quality compared to state-of-the-art methods. 

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5. Is your neighbor your friend? Scan methods for spatial social network hotspot detection

   https://doi.org/10.1111/tgis.13050  

   Liang, Xiaofan; Baker, Joshua; DellaPosta, Daniel; Andris, Clio (April 2023, Transactions in GIS)

   Abstract GIS analyses use moving window methods and hotspot detection to identify point patterns within a given area. Such methods can detect clusters of point events such as crime or disease incidences. Yet, these methods do not account forconnectionsbetween entities, and thus, areas with relatively sparse event concentrations but high network connectivity may go undetected. We develop two scan methods (i.e., moving window or focal processes), EdgeScan and NDScan, for detecting local spatial‐social connections. These methods capture edges and network density, respectively, for each node in a given focal area. We apply methods to a social network of Mafia members in New York City in the 1960s and to a 2019 spatial network of home‐to‐restaurant visits in Atlanta, Georgia. These methods successfully capture focal areas where Mafia members are highly connected and where restaurant visitors are highly local; these results differ from those derived using traditional spatial hotspot analysis using the Getis–Ord Gi* statistic. Finally, we describe how these methods can be adapted to weighted, directed, and bipartite networks and suggest future improvements. 

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