An Integrated Bus and Taxi Routes for a Mobile Trip Planning System

With the popular usage of Google Maps and smart phones, more and more people are using smart phones to surf and inquire about travel information. As a result, every major city plans to push the existing online public transportation trip planning system beyond traditional computer users to mobile phone users. The trip planning system is based on the starting and ending points that a user inputs, and guides the user to take a bus or metro through an electronic map interface. The system usually provides different kind of alternative travel routes with the estimated time of arrival. However, people who use the public transport system may encounter some uncertainties, such as long waiting times, long routes, long walking distances, etc. In each big city, the taxi is a universal transport vehicle which is available at almost anytime, anywhere. Taxis can save passengers’ walking distance and travel time with a deficit of high cost. Therefore, we design a trip planning system to unify the Taipei public transportation system with taxis. The users can inquire of a travel route through the mobile phones. This system uses Google Maps as a base map. The users assign an upper limit of fare which they are willing to pay. The system will balance between travel time and travel cost to obtain a route which may combine usage of the bus and taxi. Because of the high density of bus stations in Taipei city, the route search may consume a lot of system resources. We propose an improvement method to eliminate some intermediate bus stations in route search processing

Three Extensions of Tong and Richardson’s Algorithm for Finding the Optimal Path in Schedule-Based Railway Networks

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Scalable Schedule-Aware Bundle Routing

This thesis introduces approaches providing scalable delay-/disruption-tolerant routing capabilities in scheduled space topologies. The solution is developed for the requirements derived from use cases built according to predictions for future space topology, like the future Mars communications architecture report from the interagency operations advisory group. A novel routing algorithm is depicted to provide optimized networking performance that discards the scalability issues inherent to state-of-the-art approaches. This thesis also proposes a new recommendation to render volume management concerns generic and easily exchangeable, including a new simple management technique increasing volume awareness accuracy while being adaptable to more particular use cases. Additionally, this thesis introduces a more robust and scalable approach for internetworking between subnetworks to increase the throughput, reduce delays, and ease configuration thanks to its high flexibility.:1 Introduction 1.1 Motivation 1.2 Problem statement 1.3 Objectives 1.4 Outline 2 Requirements 2.1 Use cases 2.2 Requirements 2.2.1 Requirement analysis 2.2.2 Requirements relative to the routing algorithm 2.2.3 Requirements relative to the volume management 2.2.4 Requirements relative to interregional routing 3 Fundamentals 3.1 Delay-/disruption-tolerant networking 3.1.1 Architecture 3.1.2 Opportunistic and deterministic DTNs 3.1.3 DTN routing 3.1.4 Contact plans 3.1.5 Volume management 3.1.6 Regions 3.2 Contact graph routing 3.2.1 A non-replication routing scheme 3.2.2 Route construction 3.2.3 Route selection 3.2.4 Enhancements and main features 3.3 Graph theory and DTN routing 3.3.1 Mapping with DTN objects 3.3.2 Shortest path algorithm 3.3.3 Edge and vertex contraction 3.4 Algorithmic determinism and predictability 4 Preliminary analysis 4.1 Node and contact graphs 4.2 Scenario 4.3 Route construction in ION-CGR 4.4 Alternative route search 4.4.1 Yen’s algorithm scalability 4.4.2 Blocking issues with Yen 4.4.3 Limiting contact approaches 4.5 CGR-multicast and shortest-path tree search 4.6 Volume management 4.6.1 Volume obstruction 4.6.2 Contact sink 4.6.3 Ghost queue 4.6.4 Data rate variations 4.7 Hierarchical interregional routing 4.8 Other potential issues 5 State-of-the-art and related work 5.1 Taxonomy 5.2 Opportunistic and probabilistic approaches 5.2.1 Flooding approaches 5.2.2 PROPHET 5.2.3 MaxProp 5.2.4 Issues 5.3 Deterministic approaches 5.3.1 Movement-aware routing over interplanetary networks 5.3.2 Delay-tolerant link state routing 5.3.3 DTN routing for quasi-deterministic networks 5.3.4 Issues 5.4 CGR variants and enhancements 5.4.1 CGR alternative routing table computation 5.4.2 CGR-multicast 5.4.3 CGR extensions 5.4.4 RUCoP and CGR-hop 5.4.5 Issues 5.5 Interregional routing 5.5.1 Border gateway protocol 5.5.2 Hierarchical interregional routing 5.5.3 Issues 5.6 Further approaches 5.6.1 Machine learning approaches 5.6.2 Tropical geometry 6 Scalable schedule-aware bundle routing 6.1 Overview 6.2 Shortest-path tree routing for space networks 6.2.1 Structure 6.2.2 Tree construction 6.2.3 Tree management 6.2.4 Tree caching 6.3 Contact segmentation 6.3.1 Volume management interface 6.3.2 Simple volume manager 6.3.3 Enhanced volume manager 6.4 Contact passageways 6.4.1 Regional border definition 6.4.2 Virtual nodes 6.4.3 Pathfinding and administration 7 Evaluation 7.1 Methodology 7.1.1 Simulation tools 7.1.2 Simulator extensions 7.1.3 Algorithms and scenarios 7.2 Offline analysis 7.3 Eliminatory processing pressures 7.4 Networking performance 7.4.1 Intraregional unicast routing tests 7.4.2 Intraregional multicast tests 7.4.3 Interregional routing tests 7.4.4 Behavior with congestion 7.5 Requirement fulfillment 8 Summary and Outlook 8.1 Conclusion 8.2 Future works 8.2.1 Next development steps 8.2.2 Contact graph routin

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

Three Extensions of Tong and Richardson’s Algorithm for Finding the Optimal Path in Schedule-Based Railway Networks

High-speed railways have been developing quickly in recent years and have become a main travel mode between cities in many countries, especially China. Studying passengers’ travel choices on high-speed railway networks can aid the design of efficient operations and schedule plans. The Tong and Richardson algorithm that is used in this model offers a promising method for finding the optimal path in a schedule-based transit network. However, three aspects of this algorithm limit its application to high-speed railway networks. First, these networks have more complicated common line problems than other transit networks. Without a proper treatment, the optimal paths cannot be found. Second, nonadditive fares are important factors in considering travel choices. Incorporating these factors increases the searching time; improvement in this area is desirable. Third, as high-speed railways have low-frequency running patterns, their passengers may prefer to wait at home or at the office instead of at the station. Thus, consideration of a waiting penalty is needed. This paper suggests three extensions to improve the treatments of these three aspects, and three examples are presented to illustrate the applications of these extensions. The improved algorithm can also be used for other transit systems. Document type: Articl

Short Run Transit Route Planning Decision Support System Using a Deep Learning-Based Weighted Graph

Public transport routing plays a crucial role in transit network design, ensuring a satisfactory level of service for passengers. However, current routing solutions rely on traditional operational research heuristics, which can be time-consuming to implement and lack the ability to provide quick solutions. Here, we propose a novel deep learning-based methodology for a decision support system that enables public transport (PT) planners to identify short-term route improvements rapidly. By seamlessly adjusting specific sections of routes between two stops during specific times of the day, our method effectively reduces times and enhances PT services. Leveraging diverse data sources such as GTFS and smart card data, we extract features and model the transportation network as a directed graph. Using self-supervision, we train a deep learning model for predicting lateness values for road segments. These lateness values are then utilized as edge weights in the transportation graph, enabling efficient path searching. Through evaluating the method on Tel Aviv, we are able to reduce times on more than 9\% of the routes. The improved routes included both intraurban and suburban routes showcasing a fact highlighting the model's versatility. The findings emphasize the potential of our data-driven decision support system to enhance public transport and city logistics, promoting greater efficiency and reliability in PT services

A computation method on time-dependent accessibility of urban rail transit networks for the last service

Urban rail transit networks seldom provide 24-hour service. The last train is the latest chance for passengers. If passengers arrive too late to catch the last train, the path becomes inaccessible. The network accessibility thus varies depending on the departure time of passenger trips. This paper focuses on the computation method on the time-dependent accessibility of urban rail transit networks in order to facilitate the itinerary planning of passengers. A label setting algorithm is first designed to calculate the latest possible times for Origin–Destination (O–D) pairs, which is the latest departure times of passengers from the origins such that the destinations can be reach successfully. A searching approach is then developed to find the shortest accessible path at any possible departure times. The method is applied in a real-world metro network. The results show that the method is a powerful tool in solving the service accessibility problem. It has the ability to allow passengers to plan an optimal itinerary. Comparison analysis indicates that the proposed method can provide exact solutions in much shorter time, compared with a path enumeration method. Extensive tests on a set of random networks indicate that the method is efficient enough in practical applications. The execution time for an O–D pair on a personal computer with 2.8 GHZ CPU and 4GB of RAM is only 1.2 s for urban rail transit networks with 100 transfer stations

Development and demonstration of an on-board mission planner for helicopters

Mission management tasks can be distributed within a planning hierarchy, where each level of the hierarchy addresses a scope of action, and associated time scale or planning horizon, and requirements for plan generation response time. The current work is focused on the far-field planning subproblem, with a scope and planning horizon encompassing the entire mission and with a response time required to be about two minutes. The far-feld planning problem is posed as a constrained optimization problem and algorithms and structural organizations are proposed for the solution. Algorithms are implemented in a developmental environment, and performance is assessed with respect to optimality and feasibility for the intended application and in comparison with alternative algorithms. This is done for the three major components of far-field planning: goal planning, waypoint path planning, and timeline management. It appears feasible to meet performance requirements on a 10 Mips flyable processor (dedicated to far-field planning) using a heuristically-guided simulated annealing technique for the goal planner, a modified A* search for the waypoint path planner, and a speed scheduling technique developed for this project