Supporting route choice via real-time visual traffic information and counterfactual arrival times
Creators: name: Kwak, Daehan
Publisher: name: No Publisher Supplied
Year: 2017

description: Mobility plays an integral role in modern lives, yet with the ever-expanding number of cars, traffic congestion poses various negative effects, causing vast economic loss, air pollution, and commuter stress. As live traffic information is becoming ubiquitous, route guidance systems are used to inform drivers of route capacities to avoid traffic congestion. Navigation systems compare several different routes and provide the user with options to choose from, from a list of best possible route recommendations. Drivers’ route choice decisions are typically based on the route that minimizes their travel cost (e.g. travel time). However, there are three main limitations for route guidance and information systems. First, as travel time reliability plays an influential role in the driver’s route choice decision-making, the difference in the travel time estimations and/or recommended routes may vary across navigation systems, which can contribute to the uncertainty in the route choice. Second, as the estimated travel time is the dominant deciding factor in route choice, the impact of uncertain, inaccurate, and variable travel time estimations can render it useless, negatively influencing the drivers’ compliance to the information system’s recommended route. Third, as drivers cannot assess and compare their actual route choice to the non-chosen foregone alternatives, they face frequent dilemmas over their route-choice decisions, especially when route alternatives recommended by navigation systems are not consistent with their own previous driving experiences.    In this dissertation, our focus is to explore these three limitations. First, we present a comparative analysis on the route recommendations given from four popular online map providers: Google Maps, HERE, MapQuest and Bing Maps. We analyze traffic data collected from all four of the different map providers for 71 days for two cities, each with two origin-destination pairs. Statistical analysis show that the estimated travel times on identical routes are significantly different among the map providers.  This in itself has the potential to create uncertainty in route choices and travel time variability, in addition to a decrease in the credibility and compliance with the map provider’s route choice. Second, to complement the deciding factors (e.g., Estimated Time of Arrival (ETA)) in route decisions, we propose a system called Social Vehicle Navigation. This system incorporates a secondary level of detail into the vehicle navigation system by providing other semantically rich information that drivers can share with one another. This user‐shared visual traffic information assists in the decision‐making process and also improves the efficacy in route determinations. Third, we introduce a rationale for counterfactual thinking in route choice, where drivers receive feedback information about the actual travel times on forgone alternatives (i.e. non-chosen routes), so that at the end of the day, drivers have the ability to exercise reinforced learning and self-assessments of their route choices. We propose DoppelDriver, a system that offers a direct, actual travel time comparison among chosen and non-chosen routes, which determines the actual travel times from probe participatory vehicles on the non-chosen routes.    The main conclusion of this dissertation is that existing navigation systems have limitations and can potentially introduce uncertainty in route choice. To support and improve the driving experience, we address the use of visual traffic information for pre-trip route choice and the use of counterfactual travel times as post-choice feedback information on the forgone alternatives.