Smart City Gnosys
Smart city article details
| Title | Centrally Coordinated Routing Of Freight In Smart Cities |
|---|---|
| ID_Doc | 13563 |
| Authors | Chen P.; Ioannou P. |
| Year | 2024 |
| Published | Springer Tracts on Transportation and Traffic, 21 |
| DOI | http://dx.doi.org/10.1007/978-3-031-64769-7_13 |
| Abstract | The efficient routing and distribution of goods are vital to the survival and sustainability of any smart city. Currently, different truck companies route their trucks on a road network that is often congested and unbalanced in time and space with respect to traffic load distribution. The lack of coordination among truck operators often leads to long waiting times at pick-up and drop-off points and/or along popular routes that trucks usually follow due to overlaps. Due to connectivity and the Internet of Things new opportunities open for centrally coordinated solutions in order to achieve better load balancing in space and time for trucks across the road network. In this chapter, we present a centrally coordinated approach for routing freight in urban environments where truck traffic loads are balanced in time and space in an effort to improve mobility and reduce cost. We assume that freight is moved by trucks using the road network and truck fleets consist of a mix of diesel and electric trucks. The electric trucks add the constraint of charging time, charging station locations and driving range that is less than that of diesel. The routing problem is formulated as an optimization problem with several constraints. A co-simulation load-balancing approach is used to generate routes for trucks that reduce the overall cost. The co-simulation approach employs a traffic simulator in a feedback loop that replaces simple mathematical models often used to predict the states of the network. The simulation model captures the complexity and dynamics of the network and generates predicted states that are used to generate optimum routes and achieve load balancing. Numerical experiments are performed using a traffic simulator of the Los Angeles/Long Beach Metropolitan road traffic network that includes two major ports. The simulation results demonstrate strong potential for the proposed centralized truck routing system to reduce the impact of trucks on traffic and make their routing more efficient. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. |
| Author Keywords |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
5633  | 0.863 | Dinh H.D. | A Transportation Optimization Model For Solving The Single Delivery Truck Routing Problem With The Alldifferent Constraint In Ms Excel | Advances in 21st Century Human Settlements (2021) |
| 34303 | 0.859 | Boysen N.; Briskorn D.; Rupp J.; Schwerdfeger S. | Jam In The Tunnel: On Urban Freight Tunnels, Their Operational Scheduling, And Unused Transport Capacity | Service Science, 16, 3 (2024) |
| 62029 | 0.856 | Carroll T.; Cheng A.M.K.; Dai G. | Work In Progress: A Solution Based On Dynamic User Equilibrium Toward The Selfless Traffic Routing Model | Proceedings of the IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS, 2022-May (2022) |
| 33099 | 0.85 | Adelantado F.; Ammouriova M.; Herrera E.; Juan A.A.; Shinde S.S.; Tarchi D. | Internet Of Vehicles And Real-Time Optimization Algorithms: Concepts For Vehicle Networking In Smart Cities | Vehicles, 4, 4 (2022) |