Route Planning in Transportation Networks

We survey recent advances in algorithms for route planning in transportation networks. For road networks, we show that one can compute driving directions in milliseconds or less even at continental scale. A variety of techniques provide different trade-offs between preprocessing effort, space requirements, and query time. Some algorithms can answer queries in a fraction of a microsecond, while others can deal efficiently with real-time traffic. Journey planning on public transportation systems, although conceptually similar, is a significantly harder problem due to its inherent time-dependent and multicriteria nature. Although exact algorithms are fast enough for interactive queries on metropolitan transit systems, dealing with continent-sized instances requires simplifications or heavy preprocessing. The multimodal route planning problem, which seeks journeys combining schedule-based transportation (buses, trains) with unrestricted modes (walking, driving), is even harder, relying on approximate solutions even for metropolitan inputs.Comment: This is an updated version of the technical report MSR-TR-2014-4, previously published by Microsoft Research. This work was mostly done while the authors Daniel Delling, Andrew Goldberg, and Renato F. Werneck were at Microsoft Research Silicon Valle

Frontiers In Operations Research For Overcoming Barriers To Vehicle Electrification

Electric vehicles (EVs) hold many promises including diversification of the transportation energy feedstock and reduction of greenhouse gas and other emissions. However, achieving large-scale adoption of EVs presents a number of challenges resulting from a current lack of supporting infrastructure and difficulties in overcoming technological barriers. This dissertation addresses some of these challenges by contributing to the advancement of theories in the areas of network optimization and mechanism design. To increase the electric driving range of plug-in hybrid electric vehicles (PHEVs), we propose a powertrain energy management control system that exploits energy efficiency dif- ferences of the electric machine and the internal combustion engine during route planning. We introduce the Energy-Efficient Routing problem (EERP) for PHEVs, and formulate this problem as a new class of the shortest path problem. We prove that the EERP is NP-complete. We then propose two exact algorithms that find optimal solutions by exploiting the transitive structure inherent in the network. To tackle the intractability of the problem, we proposed a Fully Polynomial Time Approximation Scheme (FPTAS). From a theoretic perspective, the proposed two-phase approaches improve the state-of-the-art to optimally solving shortest path problems on general constrained multi-graph networks. These novel approaches are scalable and offer broad potential in many network optimization problems. In the context of vehicle routing, this is the first study to take into account energy efficiency difference of different operating modes of PHEVs during route planning, which is a high level powertrain energy management procedure. Another challenge for EV adoption is the inefficiency of current charging systems. In addition, high electricity consumption rates of EVs during charging make the load manage- ment of micro grids a challenge. We proposed an offline optimal mechanism for scheduling and pricing of electric vehicle charging considering incentives of both EV owners and utility companies. In the offline setting, information about future supply and demand is known to the scheduler. By considering uncertainty about future demand, we then designed a family of online mechanisms for real-time scheduling of EV charging. A fundamental problem with significant economic implications is how to price the charging units at different times under dynamic demand. We propose novel bidding based mechanisms for online scheduling and pricing of electric vehicle charging. The proposed preemption-aware charging mechanisms consider incentives of both EV drivers and grid operators. We also prove incentive-compatibility of the mechanisms, that is, truthful reporting is a dominant strategy for self-interested EV drivers. The proposed mechanisms demonstrate the benefits of electric grid load management, revenue maximization, and quick response, key attributes when providing online charging services

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio

Complex Network Analysis and the Applications in Vehicle Delay-Tolerant Networks

Vehicle Delay Tolerant Networks (VDTNs) is a particular kind of Delay Tolerant Networks (DTNs), where vehicles equipped with transmission capabilities are interconnected to form Vehicle NETworks (VNETs). Some applications and services on the top of VDTNs have raised a lot of attention, especially by providing information about weather conditions, road safety, traffic jams, speed limit, and even video streamings without the need of infrastructures. However, due to features such as high vehicle mobility, dynamic scenarios, sparsity of vehicles, short contact durations, disruption and intermittent connectivity and strict requirements for latency, many VDTNs do not present satisfactory performance, because no path exists between a source and its target. In this dissertation, we propose three routing methods to solve the problem as follows. Our first VDTN system focuses on the multi-copy routing in Vehicle Delay Tolerant Networks (VDTNs). Multi-copy routing can balance the network congestion caused by broadcasting and the efficiency limitation in single-copy routing. However, the different copies of each packet search the destination node independently in current multi-copy routing algorithms, which leads to a low utilization of copies since they may search through the same path repeatedly without cooperation. To solve this problem, we propose a fractal Social community based efficient multi-coPy routing in VDTNs, namely SPread. First, we measure social network features in Vehicle NETworks (VNETs). Then, by taking advantage of weak ties and fractal structure feature of the community in VNETs, SPread carefully scatters different copies of each packet to different communities that are close to the destination community, thus ensuring that different copies search the destination community through different weak ties. For the routing of each copy, current routing algorithms either fail to exploit reachability information of nodes to different nodes (centrality based methods) or only use single-hop reachability information (community based methods), e.g., similarity and probability. Here, the reachability of node $i$ to a destination $j$ (a community or a node) means the possibility that a packet can reach $j$ through $i$. In order to overcome above drawbacks, inspired by the personalized PageRank algorithm, we design new algorithms for calculating multi-hop reachability of vehicles to different communities and vehicles dynamically. Therefore, the routing efficiency of each copy can be enhanced. Finally, extensive trace-driven simulation demonstrates the high efficiency of SPread in comparison with state-of-the-art routing algorithms in DTNs. However, in SPread, we only consider the VNETs as complex networks and fail to use the unique location information to improve the routing performance. We believe that the complex network knowledge should be combined with special features of various networks themselves in order to benefit the real application better. Therefore, we further explore the possibility to improve the performance of VDTN system by taking advantage of the special features of VNETs. We first analyze vehicle network traces and observe that i) each vehicle has only a few active sub-areas that it frequently visits, and ii) two frequently encountered vehicles usually encounter each other in their active sub-areas. We then propose Active Area based Routing method (AAR) which consists of two steps based on the two observations correspondingly. AAR first distributes a packet copy to each active sub-area of the target vehicle using a traffic-considered shortest path spreading algorithm, and then in each sub-area, each packet carrier tries to forward the packet to a vehicle that has high encounter frequency with the target vehicle. Furthermore, we propose a Distributed AAR (DAAR) to improve the performance of AAR. Extensive trace-driven simulation demonstrates that AAR produces higher success rates and shorter delay in comparison with the state-of-the-art routing algorithms in VDTNs. Also, DAAR has a higher success rate and a lower average delay compared with AAR since information of dynamic active sub-areas tends to be updated from time to time, while the information of static active sub-areas may be outdated due to the change of vehicles\u27 behaviors. Finally, we try to combine different routing algorithms together and propose a DIstributed Adaptive-Learning routing method for VDTNs, namely DIAL, by taking advantages of the human beings communication feature that most interactions are generated by pairs of people who interacted often previously. DIAL consists of two components: the information fusion based routing method and the adaptive-learning framework. The information fusion based routing method enables DIAL to improve the routing performance by sharing and fusing multiple information without centralized infrastructures. Furthermore, based on the information shared by information fusion based routing method, the adaptive-learning framework enables DIAL to design personalized routing strategies for different vehicle pairs without centralized infrastructures. Therefore, DIAL can not only share and fuse multiple information of each vehicle without centralized infrastructures, but also design each vehicle pair with personalized routing strategy. Extensive trace-driven simulation demonstrates that DIAL has better routing success rate, shorter average delays and the load balance function in comparison with state-of-the-art routing methods which need the help of centralized infrastructures in VDTNs