Multi-destination vehicular route planning with parking and traffic constraints

Hakeem, Abeer; Gehani, Narain; Ding, Xiaoning; Curtmola, Reza; Borcea, Cristian

doi:10.1145/3360774.3360824

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…Abeer et al, [15] proposed an algorithm to serve multiple destination requests to plan the route considering real-time traffic conditions and the free parking places in the city to find the minimum time for the driver to travel. The results of the algorithms shown the best performance compared to others.…”

Section: Literature Reveiwmentioning

confidence: 99%

“…Consider indoor environment to find the shortest path for multiple destination like in warehouses or libraries when a customer needs multiple items to search Abeer et al, [15] 2019 Consider real-time traffic conditions in the city and free parking places for drivers Eric et al, [16] 2011 Use cluster strategy to cluster the destinations into several destination clusters…”

Section: Google Map Application For Multiple Destination Route Planningmentioning

confidence: 99%

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Route Discovery Development for Multiple Destination Using Artificial Ant Colony

Shukur¹,

Ismael

2022

Cihan U Erbil SCI J

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Smart cities need a smart applications for the citizen, not just digital devices. Smart applications will provide a decision-making to users by using artificial intelligence. Many real-world services for online shopping and delivery systems were used and attracted customers, especially after the Covid-19 pandemics when people prefer to keep social distance and minimize social places visiting. These services need to discover the shortest path for the delivery driver to visit multiple destinations and serve the customers. The aim of this research is to develop the route discovery for multiple-destination by using ACO Algorithm for Multiple destination route planning. ACO Algorithm for Multiple destination route planning develops the Google MAP application to optimize the route when it is used for multiple destinations and when the route is updated with a new destination. The results show improvement in the multiple destination route discovery when the shortest path and the sequence order of cities are found. In conclusion, the ACO Algorithm for Multiple destination route planning simulation results could be used with the Google Map application and provide an artificial decision for the citizen of Erbil city. Finally, we discuss our vision for future development.

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Section: Literature Reveiwmentioning

confidence: 99%

Section: Google Map Application For Multiple Destination Route Planningmentioning

confidence: 99%

Route Discovery Development for Multiple Destination Using Artificial Ant Colony

Shukur¹,

Ismael

2022

Cihan U Erbil SCI J

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“…Figure 2c shows the third step that calculates the shortest path between s and y, Figure 2d also shows the shortest path between x and z. In Figure 2e, all the calculated shortest paths among s and the destination nodes w, x, y, and z [18]. Here, the possible number of shortest paths is four.…”

Section: S = S U{vm} L = L\{vm}mentioning

confidence: 99%

An Efficient Shortest Path Algorithm: Multi-Destinations in an Indoor Environment

et al. 2021

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The shortest path-searching with the minimal weight for multiple destinations is a crucial need in an indoor applications, especially in supermarkets, warehouses, libraries, etc. However, when it is used for multiple item searches, its weight becomes higher as it searches only the shortest path between the single sources to each destination item separately. If the conventional Dijkstra algorithm is modified to multi-destination mode then the weight is decreased, but the output path is not considered as the real shortest path among multiple destinations items. Our proposed algorithm is more efficient for finding the shortest path among multiple destination items with minimum weight, compared to the single source single destination and modified multi-destinations of Dijkstra’s algorithm. In this research, our proposed method has been validated by real-world data as well as by simulated random solutions. Our advancement is more applicable in indoor environment applications based on multiple items or destinations searching.

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“…A Multi-Destination Vehicle Route Planning (MDVRP) approach was proposed in [ 37 ] to utilize the travel time for drivers. This approach consists of two items: a server in the cloud that utilizes the paths by identifying the best path to reach the destinations, and a mobile application for the drivers to present the real-time path and navigate the drivers to their destinations.…”

Section: Related Workmentioning

confidence: 99%

A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City

Abdeen

Nemer

Sheltami

2021

Sensors

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Parking in heavily populated areas has been considered one of the main challenges in the transportation systems for the past two decades given the limited parking resources, especially in city centres. Drivers often waste long periods of time hunting for an empty parking spot, which causes congestion and consumes energy during the process. Thus, finding an optimal parking spot depends on several factors such as street traffic congestion, trip distance/time, the availability of a parking spot, the waiting time on the lot gate, and the parking fees. Designing a parking spot allocation algorithm that takes those factors into account is crucial for an efficient and high-availability parking service. We propose a smart routing and parking algorithm to allocate an optimal parking space given the aforementioned limiting factors. This algorithm supports choosing the appropriate travel route and parking lot while considering the real-time street traffic and candidate parking lots. A multi-objective function is introduced, with varying weights of the five factors to produce the optimal parking spot with the least congested route while achieving a balanced utilization for candidate parking lots and a balanced traffic distribution. A queueing model is also developed to investigate the availability rate in candidate parking lots while considering the arrival rate, departure rate, and the lot capacity. To evaluate the performance of the proposed algorithm, simulation scenarios have been performed for different cases of high and low traffic intensity rates. We have tested the algorithm on in-city parking facility in the city of Al Madinah as a case study. The results show that the proposed algorithm is effective in achieving a balanced utilization of the parking lots, reducing traffic congestion rates on all routes to candidate parking lots, and minimizing the driving time to the assigned parking spot. Additionally, the proposed algorithm outperforms the MADM algorithm in terms of the selected three metrics for the five periods.

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Multi-destination vehicular route planning with parking and traffic constraints

Cited by 8 publications

References 15 publications

Route Discovery Development for Multiple Destination Using Artificial Ant Colony

Route Discovery Development for Multiple Destination Using Artificial Ant Colony

An Efficient Shortest Path Algorithm: Multi-Destinations in an Indoor Environment

A Balanced Algorithm for In-City Parking Allocation: A Case Study of Al Madinah City

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