Traffic Congestion Alleviation over Dynamic Road Networks: Continuous Optimal Route Combination for Trip Query Streams

Route planning and recommendation have attracted much attention for decades. In this paper, we study a continuous optimal route combination problem: Given a dynamic road network and a stream of trip queries, we continuously find an optimal route combination for each new query batch over the query stream such that the total travel time for all routes is minimized. Each route corresponds to a planning result for a particular trip query in the current query batch. Our problem targets a variety of applications, including traffic-flow management, real-time route planning and continuous congestion prevention. The exact algorithm bears exponential time complexity and is computationally prohibitive for application scenarios in dynamic traffic networks. To address this problem, a self-aware batch processing algorithm is developed in this paper. Extensive experiments offer insight into the accuracy and efficiency of our proposed algorithms.

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Towards Alleviating Traffic Congestion: Optimal Route Planning for Massive-Scale Trips

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/470 ◽

2020 ◽

Author(s):

Ke Li ◽

Lisi Chen ◽

Shuo Shang

Keyword(s):

Traffic Congestion ◽

Road Network ◽

Route Planning ◽

Exact Algorithm ◽

Optimal Route ◽

Time Cost ◽

Traffic Flow Management ◽

Massive Scale ◽

Global Travel ◽

Insight Into

We investigate the problem of optimal route planning for massive-scale trips: Given a traffic-aware road network and a set of trip queries Q, we aim to find a route for each trip such that the global travel time cost for all queries in Q is minimized. Our problem is designed for a range of applications such as traffic-flow management, route planning and congestion prevention in rush hours. The exact algorithm bears exponential time complexity and is computationally prohibitive for application scenarios in dynamic traffic networks. To address the challenge, we propose a greedy algorithm and an epsilon-refining algorithm. Extensive experiments offer insight into the accuracy and efficiency of our proposed algorithms.

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User-Optimal and System-Optimal Route Choices for a Large Road Network

Review of Network Economics ◽

10.2202/1446-9022.1058 ◽

2004 ◽

Vol 3 (4) ◽

Cited By ~ 12

Author(s):

David Boyce ◽

Qian Xiong

Keyword(s):

Travel Time ◽

Road Network ◽

Optimal Solution ◽

Optimal Route ◽

Choice Problem ◽

Vehicle Miles Traveled ◽

Peak Period ◽

The Road ◽

System Optimal ◽

Total Travel Time

Solutions to the route choice problem for assumptions of user-optimality and system-optimality are presented for the road network of the Chicago region. Regionwide results show a 5% decrease in total travel time would be achieved during the morning peak period, if a system-optimal solution based on travel times were implemented. Among the costs of this solution is a 1.5% increase in vehicle-miles traveled. Findings for differences in link flows and individual origin-destination pairs complete the paper.

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An Air Route Network Planning Model of Logistics UAV Terminal Distribution in Urban Low Altitude Airspace

Sustainability ◽

10.3390/su132313079 ◽

2021 ◽

Vol 13 (23) ◽

pp. 13079

Author(s):

Shan Li ◽

Honghai Zhang ◽

Zhuolun Li ◽

Hao Liu

Keyword(s):

Traffic Congestion ◽

Urban Areas ◽

Network Planning ◽

Search Time ◽

Route Planning ◽

Planning Model ◽

Optimal Route ◽

Logistics Distribution ◽

Route Network ◽

Low Altitude

Traditional terminal logistics distribution in urban areas is mainly concentrated on the ground, which leads to increasingly serious air pollution and traffic congestion. With the popularization of unmanned aerial vehicle (UAV) techniques and the reform of low altitude airspace, terminal logistics distribution is expected to be carried out by drones. Therefore, it is of great significance to construct a reasonable air route network for logistics UAV to ensure the safety and efficiency of operations. In this paper, a single route planning model and an air route network planning model for UAV were constructed by fully considering the complex urban low altitude environment, the flight performance of UAV and the characteristics of logistics tasks to regulate the flights of drones. Then, taking Jiangjun Road Campus of Nanjing University of Aeronautics and Astronautics as an example, the improved cellular automata (CA) was adopted to search for the optimal route between different waypoints, and the optimal spanning tree algorithm was used to construct the route network. The experimental results demonstrated that the improved CA could greatly reduce search time and obtain the optimal route while enhancing safety. With the satisfaction of the voyage, the needs of logistics and distribution constraints, a network that had smaller intersection points and redundancy was generated. The models and core ideas proposed in this paper can not only regulate operation of drones but also provide a solid foundation for the distribution of logistics UAV in the future.

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Personalized Route Planning System Based on Driver Preference

Sensors ◽

10.3390/s22010011 ◽

2021 ◽

Vol 22 (1) ◽

pp. 11

Author(s):

Ren Wang ◽

Mengchu Zhou ◽

Kaizhou Gao ◽

Ahmed Alabdulwahab ◽

Muhyaddin J. Rawa

Keyword(s):

Road Network ◽

Route Planning ◽

Multiple Objectives ◽

Experimental Results ◽

Planning System ◽

Search Process ◽

Navigation Systems ◽

Optimal Route ◽

Computation Cost ◽

Route Navigation

At present, most popular route navigation systems only use a few sensed or measured attributes to recommend a route. Yet the optimal route considered by drivers needs be based on multiple objectives and multiple attributes. As a result, these existing systems based on a single or few attributes may fail to meet such drivers’ needs. This work proposes a driver preference-based route planning (DPRP) model. It can recommend an optimal route by considering driver preference. We collect drivers’ preferences, and then provide a set of routes for their choice when they need. Next, we present an integrated algorithm to solve DPRP, which speeds up the search process for recommending the best routes. Its computation cost can be reduced by simplifying a road network and removing invalid sub-routes. Experimental results demonstrate its effectiveness.

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Impact of the urban construction solutions on the safety population

Issues of Risk Analysis ◽

10.32686/1812-5220-2020-17-2-66-73 ◽

2020 ◽

Vol 17 (2) ◽

pp. 66-73

Author(s):

R. D. Oktyabrskiy

Keyword(s):

Traffic Congestion ◽

Urban Population ◽

Road Network ◽

Residential Development ◽

Emergency Situations ◽

Urban Construction ◽

Safety Population ◽

Peace And War ◽

The City

The article is devoted to the justification of the need to reduce the population density in the residential development of cities. The analysis of vulnerability of the urban population from threats of emergency situations of peace and war time, and also an assessment of provision of the city by a road network is given. Proposals have been formulated to reduce the vulnerability of the urban population in the long term and to eliminate traffic congestion and congestion — jams.

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Recent advances on urban taxi sharing systems with the path arrival probability

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200402112913 ◽

2020 ◽

Vol 13 ◽

Author(s):

Dui Hongyan ◽

Zhang Chi

Keyword(s):

Failure Probability ◽

Traffic Congestion ◽

Optimal Strategy ◽

Time Windows ◽

Optimal Path ◽

Route Planning ◽

Driving Time ◽

Load Rate ◽

Travel Efficiency ◽

Arrival Probability

Background : Taxi sharing is an emerging transportation arrangement that helps improve the passengers’ travel efficiency and reduce costs. This study proposes an urban taxi sharing system. Methods: Considering each side congestion of the transport network, their corresponding reliability and failure probability are analyzed. Under the constraints of the number of passengers and their own time windows, the analysis is performed on passengers whose optimal path is inclusive. Results: According to the optimal strategy, the different passengers can be arranged into the same taxi to realize the taxi sharing. Then the shared taxi route can be optimized. Conclusion: Due to the reasonable vehicle route planning and passenger combination, these can effectively alleviate the traffic congestion, save the driving time, reduce the taxi no-load rate, and save the driving distance. At last, a numerical example is used to demonstrate the proposed method.

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Large-Scale Road Network Congestion Pattern Analysis and Prediction Using Deep Convolutional Autoencoder

Sustainability ◽

10.3390/su13095108 ◽

2021 ◽

Vol 13 (9) ◽

pp. 5108

Author(s):

Navin Ranjan ◽

Sovit Bhandari ◽

Pervez Khan ◽

Youn-Sik Hong ◽

Hoon Kim

Keyword(s):

Traffic Congestion ◽

Network Architecture ◽

Road Network ◽

Large Scale ◽

Pattern Analysis ◽

Image Data ◽

Urban Life ◽

Prediction Algorithm ◽

Rapid Urbanization ◽

Convolutional Autoencoder

The transportation system, especially the road network, is the backbone of any modern economy. However, with rapid urbanization, the congestion level has surged drastically, causing a direct effect on the quality of urban life, the environment, and the economy. In this paper, we propose (i) an inexpensive and efficient Traffic Congestion Pattern Analysis algorithm based on Image Processing, which identifies the group of roads in a network that suffers from reoccurring congestion; (ii) deep neural network architecture, formed from Convolutional Autoencoder, which learns both spatial and temporal relationships from the sequence of image data to predict the city-wide grid congestion index. Our experiment shows that both algorithms are efficient because the pattern analysis is based on the basic operations of arithmetic, whereas the prediction algorithm outperforms two other deep neural networks (Convolutional Recurrent Autoencoder and ConvLSTM) in terms of large-scale traffic network prediction performance. A case study was conducted on the dataset from Seoul city.

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