scholarly journals Personalized Route Planning System Based on Driver Preference

Sensors ◽  
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.

scholarly journals A SOFT COMPUTING APPROACH FOR SMOOTH TRAFFIC FLOW ON ROAD NETWORK

2017 ◽  
Vol 5 (4) ◽  
pp. 311-319
Author(s):  
Mrinmoyee Chattoraj ◽  
Udaya Rani V
Keyword(s):  
Traffic Congestion ◽  
Route Planning ◽  
Planning System ◽  
Navigation Systems ◽  
The Road ◽  
Smooth Flow ◽  
Congested Area ◽  
On The Road ◽  
Day By Day ◽  
Computing Approach

Route planning has an important role in navigation systems. In order to select an optimized route the traveller has to take various factors into consideration. Traffic congestion is an important factor which needs to be considered while route planning. As the numbers of vehicles are increasing on the road the traffic congestion is also increasing in an exponential manner. In a congested area the best approach to search for a route is to select an alternative path so that we can reach our destination and indirectly save some time. In the recent years route planning system has become an important area of research since the number of vehicles are increasing day-by-day but the traffic structure is un-expandable. In this paper a genetic algorithm is proposed to develop an alternate route which results in smooth flow of traffic. Genetic Algorithm’s main aim is to create an optimized path.

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

2021 ◽  
Author(s):  
Ke Li ◽  
Lisi Chen ◽  
Shuo Shang ◽  
Panos Kalnis ◽  
Bin Yao
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Route Planning ◽  
Exact Algorithm ◽  
Optimal Route ◽  
Traffic Flow Management ◽  
Total Travel Time ◽  
Query Streams ◽  
Dynamic Road Networks ◽  
Insight Into

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.

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

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.

scholarly journals The Estimation of Angular Misalignments for Ultra Short Baseline Navigation Systems. Part II: Experimental Results

2013 ◽  
Vol 66 (5) ◽  
pp. 773-787 ◽  
Author(s):  
Hsin-Hung Chen
Keyword(s):  
Trial Data ◽  
Experimental Results ◽  
Experimental Result ◽  
Alignment Error ◽  
Navigation Systems ◽  
Analytical Prediction ◽  
Short Baseline ◽  
Sea Trial ◽  
Alignment Errors ◽  
Cross Track

An algorithm of alignment calibration for Ultra Short Baseline (USBL) navigation systems was presented in the companion work (Part I). In this part (Part II) of the paper, this algorithm is tested on the sea trial data collected from USBL line surveys. In particular, the solutions to two practical problems referred to as heading deviation and cross-track error in the USBL line survey are presented. A field experiment running eight line surveys was conducted to collect USBL positioning data. The numerical results for the sea trial data demonstrated that the proposed algorithm could robustly and effectively estimate the alignment errors. Comparisons of the experimental result with the analytical prediction of roll misalignment estimation in Part I is drawn, showing good agreement. The experimental results also show that an inappropriate estimation of roll alignment error will significantly degrade the quality of estimations of heading and pitch alignment errors.

QARTA

2021 ◽  
Vol 14 (11) ◽  
pp. 2273-2282
Author(s):  
Mashaal Musleh ◽  
Sofiane Abbar ◽  
Rade Stanojevic ◽  
Mohamed Mokbel
Keyword(s):  
Machine Learning ◽  
Road Network ◽  
Contextual Information ◽  
Time Of Day ◽  
Food Delivery ◽  
Machine Learning Techniques ◽  
Navigation Systems ◽  
Query Result ◽  
Learning Techniques ◽  
Map Service

Maps services are ubiquitous in widely used applications including navigation systems, ride sharing, and items/food delivery. Though there are plenty of efforts to support such services through designing more efficient algorithms, we believe that efficiency is no longer a bottleneck to these services. Instead, it is the accuracy of the underlying road network and query result. This paper presents QARTA; an open-source full-fledged system for highly accurate and scalable map services. QARTA employs machine learning techniques to construct its own highly accurate map, not only in terms of map topology but more importantly, in terms of edge weights. QARTA also employs machine learning techniques to calibrate its query answers based on contextual information, including transportation modality, location, and time of day/week. QARTA is currently deployed in all Taxis and the third largest food delivery company in the State of Qatar, replacing the commercial map service that was in use, and responding in real-time to hundreds of thousands of daily API calls. Experimental evaluation of QARTA shows its comparable or higher accuracy than commercial services.

scholarly journals Capacitated vehicle routing problem model for carriers

2018 ◽  
Vol 12 ◽  
Author(s):  
Irma-Delia Rojas-Cuevas ◽  
Santiago-Omar Caballero-Morales ◽  
Jose-Luis Martinez-Flores ◽  
Jose-Rafael Mendoza-Vazquez
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Network Flow ◽  
Route Planning ◽  
Test Problems ◽  
Optimal Route ◽  
Routing Problem ◽  
Problem Model ◽  
Capacitated Vehicle Routing Problem ◽  
Capacitated Vehicle

Background: The Capacitated Vehicle Routing Problem (CVRP) is one of the most important transportation problems in logistics and supply chain management. The standard CVRP considers a fleet of vehicles with homogeneous capacity that depart from a warehouse, collect products from (or deliver products to) a set of customer locations (points) and return to the same warehouse. However, the operation of carrier companies and third-party transportation providers may follow a different network flow for collection and delivery. This may lead to non-optimal route planning through the use of the standard CVRP.Objective: To propose a model for carrier companies to obtain optimal route planning.Method: A Capacitated Vehicle Routing Problem for Carriers (CVRPfC) model is used to consider the distribution scenario where a fleet of vehicles depart from a vehicle storage depot, collect products from a set of customer points and deliver them to a specific warehouse before returning to the vehicle storage depot. Validation of the model’s functionality was performed with adapted CVRP test problems from the Vehicle Routing Problem LIBrary. Following this, an assessment of the model’s economic impact was performed and validated with data from a real carrier (real instance) with the previously described distribution scenario.Results: The route planning obtained through the CVRPfC model accurately described the network flow of the real instance and significantly reduced its distribution costs.Conclusion: The CVRPfC model can thus improve the competitiveness of the carriers by providing better fares to their customers, reducing their distribution costs in the process.

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