Route Planning Services Research Articles

Route Planning Service for Emergency Vehicles with Increased Accuracy and Efficiency for Online Platforms

Route Planning Service for Emergency Vehicles with Increased Accuracy and Efficiency for Online Platforms

Route Planning Through Distributed Computing by Road Side Units

Cities are embracing data-intensive applications to maximize their constrained transportation networks. Platforms such as Google offer route planning services to mitigate the effect of traffic congestion. These use remote servers that require an Internet connection, which exposes data to increased risk of network failures and latency issues. Edge computing, an alternative to centralized architectures, offers computational power at the edge that could be used for similar services. Road side units (RSU), Internet of Things (IoT) devices within a city, offer an opportunity to offload computation to the edge. To provide an environment for processing on RSUs, we introduce RSU-Edge, a distributed edge computing system for RSUs. We design and develop a decentralized route planning service over RSU-Edge. In the service, the city is divided into grids and assigned an RSU. Users send trip queries to the service and obtain routes. For maximum accuracy, tasks must be allocated to optimal RSUs. However, this overloads RSUs, increasing delay. To reduce delays, tasks may be reallocated from overloaded RSUs to its neighbors. The distance between the optimal and actual allocation causes accuracy loss due to stale data. The problem is identifying the most efficient allocation of tasks such that response constraints are met while maintaining acceptable accuracy. We created the system and present an analysis of a case study in Nashville, Tennessee that shows the effect of our algorithm on route accuracy and query response, given varying neighbor levels. We find that our system can respond to 1000 queries up to 57.17% faster, with only a model accuracy loss of 5.57% to 7.25% compared to using only optimal grid allocation.

Framework of Aspects for the Evaluation of Multimodal Journey Planners

Nowadays, several journey planners are available for users with various functionalities. The aim of this research was to provide an overview of European journey planners considering the perspective of the user. Therefore, a framework of aspects was defined, which contains aspects of route planning services, booking and payment, handled data, and supplementary information. Based on these aspects, an evaluation method was elaborated to compare and rank the journey planners. The method consists of two main steps. First, the journey planners were compared (scoring) to each other, which resulted in the general evaluation number. In the scoring step, multi criteria analysis was adapted, because it produces clear and comparable results. As a second step different user groups were introduced and, using preferences of these groups (weighting), the average evaluation number was calculated. Finally, the elaborated method presented a quantified evaluation and ranking of multimodal journey planners, which supports choosing suitable journey planners for the users.

A Collaborative System for Suitable Wheelchair Route Planning

Route planning is a challenging problem for urban computing that usually involves the processing of a huge amount of data and collaborative user feedback. Traditionally, route planning services are street-based, that is, even paths for a pedestrian are suggested in terms of streets. However, such models are not suitable for users with certain disabilities. To address this problem, we have performed a requirement analysis with a group of wheelchair-users and their companions to understand their urban mobility experience. Given that perspective, we describe in this article a sidewalk-based model to accommodate the needs for a wheelchair route planning service. The model is mathematically defined as a graph, where the vertices are the city block corners and the edges are the sidewalks or crosswalks. The edge costs are derived from important accessibility features, such as distance, path inclination, and existence and maintenance conditions of curb ramps, crosswalks, and sidewalks. The model has been designed so that user feedback is considered to help updating the model when accessibility issues are detected, by wheelchair-users and companions, or solved, by the department of city planning. We also present a route planning algorithm that provides a set of alternative routes based on accessibility conditions, and a shortcut recommender algorithm to support accessibility-related decision making by the department of city planning. Experiments, by using PgRouting and PostGIS with open data, are reported for a Brazilian city neighborhood to validate the model and the route planning service.

Rand McNally: Navigating the Wireless Landscape

Chris Barnett, director of global business solutions for Rand McNally, was deliberating how Rand McNally should respond to the emergence of wireless technologies for its traditional business of providing static maps and route-planning services. As maps became electronic, interactive, mobile, and enhanced with value-added features, Rand McNally's mapping business was gravely threatened. The opportunities for Rand McNally weren't obvious, and the pace at which wireless technology would disrupt its traditional business was also unclear. Barnett was considering three opportunities: syndicate Rand McNally's brand and mapping content to popular Web sites, become a provider of value-added services to businesses, or focus on automobile manufacturers and try to forge relationships for providing in-car mapping services.To discuss organizational design, potential responses to disruptive technologies, and market opportunity analysis in order to identify the kind of technology, organizational, and sales force restructuring required to align Rand McNally's organization with the new environment.

A Route Planner Service with Recharging Reservation: Electric Itinerary with a Click

In most countries the share of circulating electric vehicles (EV) is less than 1%. Beside cost issues, the detrimental factors that most discourage people from purchasing EVs are their limited range and the rather poor coverage area of the recharging infrastructure. This clearly indicates the need for higher investments in vehicular technologies and infrastructures; however, software services and applications also play a major role in the transition to electric mobility, e.g. to mitigate EV driver anxiety. In this paper we illustrate the recipe proposed by the European Internet of Energy (IoE) project toward interoperable services for large-scale EV mobility scenarios: it consists of a Service-Oriented Architecture (SoA) to collect and integrate data coming from the heterogeneous actors of the EV scenario thanks to a semantic data tier. On top of it, we describe the implementation of an advanced Route Planning service to compute the optimal path towards a destination according to user-defined parameters and goals; differently from other approaches, our tool integrates reservation mechanisms of charging slots, so that a complete planning of the itinerary can be produced. Effectiveness of the route planning service is evaluated on large-scale EV scenarios (e.g. the Italian Emilia-Romagna region) thanks to an innovative emulation-simulation platform.

AffectRoute – considering people’s affective responses to environments for enhancing route-planning services

Humans perceive and evaluate environments affectively. Some places are experienced as unsafe, while some others as attractive and interesting. These affective responses to environments influence people’s daily behavior and decision-making in space, e.g., choosing which route to take, or which place to visit. In this article, we report on a methodology of using people’s affective responses to environments for enhancing computer-based route planning. More specifically, we explore a crowdsourcing approach to model and collect people’s affective responses to environments; an Affect-Space-Model and a mobile application are developed to facilitate this crowdsourcing approach; a routing algorithm (named AffectRoute) is then proposed to aggregate and integrate the collected data for automatic route planning. Evaluation with human participants shows that the routes generated by considering people’s affective responses to environments are significantly preferred over the conventional shortest ones, which are employed in car navigation systems and many online route planners. In conclusion, considering people’s affective responses to environments contributes to the improvement of automatic route planning. The proposed method can be integrated into existing route-planning services (e.g., location-based services) to provide users with more satisfying routing results.