B-Planner: Night bus route planning using large-scale taxi GPS traces

2013 ◽  
Author(s):  
Chao Chen ◽  
Daqing Zhang ◽  
Zhi-Hua Zhou ◽  
Nan Li ◽  
T. Atmaca ◽  
...  
Keyword(s):  
Large Scale ◽  
Route Planning ◽  
Gps Traces ◽  
Bus Route Planning

What determines a boundary for navigating a complex street network: evidence from London taxi drivers

2021 ◽  
pp. 1-20
Author(s):  
Eva-Maria Griesbauer ◽  
Ed Manley ◽  
Daniel McNamee ◽  
Jeremy Morley ◽  
Hugo Spiers
Keyword(s):  
Urban Space ◽  
Large Scale ◽  
Route Planning ◽  
Taxi Drivers ◽  
Street Networks ◽  
Planning Models ◽  
Key Factor ◽  
Efficient Planning ◽  
Regent's Park ◽  
Insight Into

Abstract Spatial boundaries play an important role in defining spaces, structuring memory and supporting planning during navigation. Recent models of hierarchical route planning use boundaries to plan efficiently first across regions and then within regions. However, it remains unclear which structures (e.g. parks, rivers, major streets, etc.) will form salient boundaries in real-world cities. This study tested licensed London taxi drivers, who are unique in their ability to navigate London flexibly without physical navigation aids. They were asked to indicate streets they considered as boundaries for London districts or dividing areas. It was found that agreement on boundary streets varied considerably, from some boundaries providing almost no consensus to some boundaries consistently noted as boundaries. Examining the properties of the streets revealed that a key factor in the consistent boundaries was the near rectilinear nature of the designated region (e.g. Mayfair and Soho) and the distinctiveness of parks (e.g. Regent's Park). Surprisingly, the River Thames was not consistently considered as a boundary. These findings provide insight into types of environmental features that lead to the perception of explicit boundaries in large-scale urban space. Because route planning models assume that boundaries are used to segregate the space for efficient planning, these results help make predictions of the likely planning demands of different routes in such complex large-scale street networks. Such predictions could be used to highlight information used for navigation guidance applications to enable more efficient hierarchical planning and learning of large-scale environments.

scholarly journals Accurate Real-Time Traffic Speed Estimation Using Infrastructure-Free Vehicular Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-19 ◽  
Author(s):  
Zongjian He ◽  
Buyang Cao ◽  
Yan Liu
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Vehicular Networks ◽  
Route Planning ◽  
Speed Estimation ◽  
Traffic Condition ◽  
Real Time Traffic ◽  
Traffic Trace ◽  
Traffic Speed ◽  
Traffic Speed Estimation

Real-time traffic speed is indispensable for many ITS applications, such as traffic-aware route planning and eco-driving advisory system. Existing traffic speed estimation solutions assume vehicles travel along roads using constant speed. However, this assumption does not hold due to traffic dynamicity and can potentially lead to inaccurate estimation in real world. In this paper, we propose a novel in-network traffic speed estimation approach using infrastructure-free vehicular networks. The proposed solution utilizes macroscopic traffic flow model to estimate the traffic condition. The selected model only relies on vehicle density, which is less likely to be affected by the traffic dynamicity. In addition, we also demonstrate an application of the proposed solution in real-time route planning applications. Extensive evaluations using both traffic trace based large scale simulation and testbed based implementation have been performed. The results show that our solution outperforms some existing ones in terms of accuracy and efficiency in traffic-aware route planning applications.

A Study on Bus Route Planning Model

1981 ◽  
Vol 14 (2) ◽  
pp. 2385-2390
Author(s):  
K. Amano ◽  
Y. Zenitani
Keyword(s):  
Route Planning ◽  
Planning Model ◽  
Bus Route Planning

scholarly journals Efficient Data Collection using Mobile Sink Scheme in IoT

2019 ◽  
Vol 9 (1) ◽  
pp. 1360-1371
Keyword(s):  
Big Data ◽  
Energy Consumption ◽  
Data Collection ◽  
Large Scale ◽  
Performance Metrics ◽  
Route Planning ◽  
Mobile Sink ◽  
Sensor Nodes ◽  
Delivery Ratio ◽  
Energy Hole

Wireless sensor networks (WSNs) have become increasingly important in the informative development of communication technology. The growth of Internet of Things (IoT) has increased the use of WSNs in association with large scale industrial applications. The integration of WSNs with IoT is the pillar for the creation of an inescapable smart environment. A huge volume of data is being generated every day by the deployment of WSNs in smart infrastructure. The collaboration is applicable to environmental surveillance, health surveillance, transportation surveillance and many more other fields. A huge quantity of data which is obtained in various formats from varied applications is called big data. The Energy efficient big data collection requires new techniques to gather sensor-based data which is widely and densely distributed in WSNs and spread over wider geographical areas. In view of the limited range of communication and low powered sensor nodes, data gathering in WSN is a tedious task. The energy hole is another considerable issue that requires attention for efficient handling in WSN. The concept of mobile sink has been widely accepted and exploited, since it is able to effectively alleviate the energy hole problem. Scheduling a mobile sink with energy efficiency is still a challenge in WSNs time constraint implementation due to the slow speed of the mobile sink. The paper addresses the above issues and the proposal contains four-phase data collection model; the first phase is the identification of network subgroups, which are formed due to a restricted range of communication in sensor nodes in a wide network, second is clustering which is addressed on each identified subgroup for reducing energy consumption, third is efficient route planning and fourth is based on data collection. The two time-sensitive route planning schemes are presented to build a set of trajectories which satisfy the deadline constraint and minimize the overall delay. We have evaluated the performance of our schemes through simulation and compared them with the generic enhanced expectation-maximization (EEM) mobility based scenario of data collection. Simulation results reveal that our proposed schemes give much better results as compared to the generic EEM mobility approach in terms of selected performance metrics such as energy consumption, delay, network lifetime and packet delivery ratio.

scholarly journals Multiperspective Bus Route Planning in a Stackelberg Game Framework

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Xinyu Liu ◽  
Jie Yu ◽  
Xiaoguang Yang ◽  
Weijie Tan
Keyword(s):  
Service Providers ◽  
Stackelberg Game ◽  
Route Planning ◽  
Government Agencies ◽  
Fleet Size ◽  
Bisection Algorithm ◽  
Proposed Model ◽  
Bus Route Planning ◽  
The Impact

Bus route planning is a challenging task due to multiple perspective interactions among passengers, service providers, and government agencies. This paper presents a multidimensional Stackelberg-game-based framework and mathematical model to best trade off the decisions of multiple stakeholders that previous literature rarely captures, i.e., governments, service providers, and passengers, in planning a new bus route or adjusting an existing one. The proposed model features a bilevel structure with the upper level reflecting the perspective of government agencies in subsidy allocation and the lower level representing the decisions of service providers in dispatching frequency and bus fleet size design. The bilevel model is framed as a Stackelberg game where government agencies take the role of “leader” and service providers take the role of “follower” with social costs and profits set as payoffs, respectively. This Stackelberg-game-based framework can reflect the decision sequence of both participants as well as their competition or collaboration relationship in planning a bus route. The impact of such decisions on the mode and route choices of passengers is captured by a Nested Logit model. A partition-based bisection algorithm is developed to solve the proposed model. Results from a case study in Shanghai validate the effectiveness and performance of the proposed model and algorithm.

scholarly journals Trialing Innovative Technologies in Crisis Management—“Airborne and Terrestrial Situational Awareness” as Support Tool in Flood Response

2020 ◽  
Vol 10 (11) ◽  
pp. 3743 ◽  
Author(s):  
Elisa Schröter ◽  
Ralph Kiefl ◽  
Eric Neidhardt ◽  
Gaby Gurczik ◽  
Carsten Dalaff ◽  
...  
Keyword(s):  
Crisis Management ◽  
Urban Areas ◽  
Large Scale ◽  
New Technologies ◽  
Situational Awareness ◽  
Route Planning ◽  
Added Value ◽  
Aerial Image ◽  
Specific Response ◽  
Test Bed

Flooding represents the most-occurring and deadliest threats worldwide among natural disasters. Consequently, new technologies are constantly developed to improve response capacities in crisis management. The remaining challenge for practitioner organizations is not only to identify the best solution to their individual demands, but also to test and evaluate its benefit in a realistic environment before the disaster strikes. To bridge the gap between theoretic potential and actual integration into practice, the EU-funded project DRIVER+ has designed a methodical and technical environment to assess innovation in a realistic but non-operational setup through trials. The German Aerospace Center (DLR) interdisciplinary merged mature technical developments into the “Airborne and terrestrial situational awareness” system and applied it in a DRIVER+ Trial to promote a sustainable and demand-oriented R&D. Experienced practitioners assessed the added value of its modules “KeepOperational” and “ZKI” in the context of large-scale flooding in urban areas. The solution aimed at providing contextual route planning in police operations and extending situational awareness based on information derived through aerial image processing. The user feedback and systematically collected data through the DRIVER + Test-bed approved that DLR’s system could improve transport planning and situational awareness across organizations. However, the results show a special need to consider, for example, cross-domain data-fusion techniques to provide essential 3D geo-information to effectively support specific response tasks during flooding.

scholarly journals Do solitary foraging nocturnal mammals plan their routes?

2011 ◽  
Vol 7 (4) ◽  
pp. 638-640 ◽  
Author(s):  
Marine Joly ◽  
Elke Zimmermann
Keyword(s):  
Cognitive Skills ◽  
Large Scale ◽  
Deciduous Forest ◽  
Brain Size ◽  
Social Systems ◽  
Route Planning ◽  
Movement Pattern ◽  
Food Resources ◽  
Foraging Strategies ◽  
Detection Distance

Large-brained diurnal mammals with complex social systems are known to plan where and how to reach a resource, as shown by a systematic movement pattern analysis. We examined for the first time large-scale movement patterns of a solitary-ranging and small-brained mammal, the mouse lemur ( Microcebus murinus ), by using the change-point test and a heuristic random travel model to get insight into foraging strategies and potential route-planning abilities. Mouse lemurs are small nocturnal primates inhabiting the seasonal dry deciduous forest in Madagascar. During the lean season with limited food availability, these lemurs rely on few stationary food resources. We radio-tracked seven lemurs and analysed their foraging patterns. First change-points coincided with out-of-sight keystone food resources. Travel paths were more efficient in detecting these resources than a heuristic random travel model within limits of estimated detection distance. Findings suggest that even nocturnal, solitary-ranging mammals with small brains plan their route to an out-of-sight target. Thus, similar ecological pressures may lead to comparable spatial cognitive skills irrespective of the degree of sociality or relative brain size.

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