With the increasing popularity of smart phones, Parking Reservation System (PRS) becomes practical to reduce the travel time in cruising for vacant spaces. The aim of this study is to assess the impact of PRS explicitly. This paper was started with analyzing the processes of cruising for vacant spaces and making parking reservation decisions. The vehicles were divided into two categories: the intelligent vehicles and the regular ones. Only the intelligent vehicles have the ability to make a parking reservation beforehand, while the regular ones have to cruise for vacant spaces. All involved components were treated as different agents, including vehicles, parking lots, network, and management center. Based on this, agent-based simulation was introduced to evaluate the performances of the scenarios with different penetration rates. The simulation results indicate the average travel time increases with the improvement of the penetration rates for the regular vehicles. The assessment method presented in this study would assist in promoting the performances of PRS in urban areas.
The shortage of parking spaces is one of the major ignored transportation issues in urban areas [
To investigate the responding strategies of the reservation requests, Teodorović and Lučić [
Agent-based simulation is suitable to model the activities of participants in Intelligent Transportation Systems (ITS) [
The remainder of this paper is structured as follows. Section
Parking choice behavior is one of the hot issues for parking research, and the related theories and methods considering the real-time information are constantly enriched and perfected [
Without the assistance of PRS, the cruising process is presented in Figure
Process of cruising for vacant spaces.
Figure
Moreover, the perceived waiting time was calculated as follows:
For each vehicle, the comparison between the alternative parking lot and the target one is carried out to assist the parking choice decision-making. The alternative parking lot superior to the target one is expressed as
To describe driving towards the target parking lot, the spatial position is updated on the network. The updated algorithm is set as follows: if the vehicle arrives at an intersection, select the shortest route leading to the target parking lot and access the next link on the route. The shortest route is selected according to the Dijkstra algorithm [
For the intelligent vehicles, the reservation process is comparably simplified, as shown in Figure
Process of making parking reservation decisions.
Figure
Since drivers prefer to select the parking lot with vacant spaces and the parking lot status changes with time, it is essential to describe the processes of cruising for vacant spaces and making parking reservation decisions dynamically. Agent-based simulation modelled drivers’ behavior by treating each vehicle as an autonomous agent, which is extensible according to the actual project demands. With the assistance of the agent-based simulation, this paper focuses on evaluating the performances of the scenarios with different penetration rates. Additionally, the penetration rate is the ratio of the intelligent vehicles that are able to make parking reservation decisions.
To implement the agent-based simulation, agents which describe the activities of participants have to be designed. For instance, the intelligent vehicles receive real-time information and send the reservation requests via smart phones, while the regular ones have to cruise for vacant spaces. The parking and traffic information mainly includes the parking lot status and the link average speed, while the remaining information is static. Management center takes charge of replying to the reservation requests. All the involved components were treated as different agents, including vehicles, parking lots, network, and management center, as follows.
Each vehicle is treated as an autonomous agent with six attributes: origin, destination, an intelligent flag indicating whether the vehicle is an intelligent one, target parking lot, entering time, and departing time. As for the attributes, the first three are static while the rest are the dynamic ones. Moreover, the first three are known for each vehicle and the rest are updated according to the simulation conditions. The vehicle agents move forwards on the road network individually. When entering into the parking zone, the vehicular attributes are initialized. Then, the vehicles are loaded on the network and travel along the route leading to the target parking lot. If arriving at the downstream intersection, the vehicle would access the next link directly connected.
The parking lot agent also includes two types of attributes: the static and the dynamic ones. The static attributes include name, location, capacity, and fare charged for each parking lot, while the dynamic one is mainly the parking lot status. If no vacant spaces within the specified parking lot, the status becomes FULL; otherwise, the status is available.
Road network was treated as network agent, mainly consisting of eight attributes: link length, link lanes, link density, link free-flow speed, link minimal speed, link average speed, intersection position, and the connecting relationships between the links and the intersections. The link density and the average speed change with time, while the remaining attributes are static, and the link density is defined as the number of vehicles within one unit length of the link, which can be used to determine the link average speed.
Management center agent is responsible for handling the reservation requests and updating the reservation list. To reserve at least one space in the reserved parking lot and prevent the reserved spaces being illegally occupied, the rules of attaching/detaching operations are defined as follows: if the reservation requests were accepted, attach the vehicles to the reserved parking lot; otherwise, drivers make a parking choice decision according to the rule discussed. When the intelligent vehicles arrive at the reserved parking lot, they have to be detached from the parking lot. The attaching/detaching operations form the reservation list, which denotes the reservation relationships of the intelligent vehicles and the reserved parking lots at the simulation iterations. In the reservation list, the spaces are named as the reserved ones.
In the simulation environment, only vehicle agents move dynamically. The possible states of the vehicle agents are entering into the parking zone, cruising for vacant spaces or making parking reservation decisions, heading to the target parking lot, and departing from the network. By incorporating the agent design and the processes of cruising for vacant spaces and making parking reservation decisions, the agent-based simulation is illustrated in Figure
Framework of the agent-based simulation.
The overall process of the simulation is summarized as follows.
Update the parking lot status. The number of vacant spaces is computed as follows: where The parking lot status is updated according to the number of vacant spaces. If there are no vacant spaces in the parking lot, the status becomes FULL; otherwise, the status is available. Update the traffic conditions. The link density is calculated as follows: where To capture the link average speed varying with the density, Li et al. [ where Update the reservation list according to the attaching/detaching operations (as discussed in Section
The total walking distance is computed as follows:
Xujiahui CBD, Shanghai, China, was built in the 1990s, providing the comprehensive commercial services, such as shopping, entertainment, and business. The core area starts from Yishan Rd in the west and ends at Wanping Rd and starts from Guangyuan Rd in the north and ends at Lingling Rd. It covers 4.04 square kilometers, with the radius is around 1000 meters, and the center locates at an intersection intersected by five roads: Huashan Rd, Hongqiao Rd, North Caoxi Rd, Zhaojiabang Rd, and Hengshan Rd. Location and layout of Xujiahui CBD are presented in Figure
Location and layout of Xujiahui CBD, Shanghai, China.
Figure
Capacities and fees charged for the parking lots.
Parking lot index | Capacity | Fee (RMB/h) |
---|---|---|
8 | 1260 | 10 |
9 | 500 | 9 |
10 | 191 | 15 |
11 | 333 | 10 |
12 | 250 | 15 |
13 | 234 | 10 |
Network of Xujiahui CBD, Shanghai, China.
As for the penetration rates, five typical scenarios were evaluated as 0.2, 0.4, 0.6, 0.8, and 1.0. The scenario with penetration rate 0.2 was named as Scenario 1, and the remaining scenarios were denoted as Scenario 2, Scenario 3, Scenario 4, and Scenario 5 in sequence. By implementing the agent-based simulation to evaluate the five scenarios, the number of the reserved spaces was obtained and is shown in Figure
Number of the reserved spaces with the scenarios, (a) Scenario 1, (b) Scenario 2, (c) Scenario 3, (d) Scenario 4, and (e) Scenario 5.
For all the scenarios, the number of the reserved spaces increases when the target parking lot has vacant spaces and decreases under the condition that the majority of the intelligent vehicles arrive at the target parking lot. Between the trends rise and decline, most intelligent vehicles are heading to the target parking lot with the status FULL. At the stage, the indicator reaches the maximum value. Moreover, the fluctuations indicate the intelligent vehicles are able to change the reservation decisions and reserve the alternative parking lot. It is obvious that P8 and P10 are the most popular parking lots, while the remaining parking lots are comparably less utilized.
The parking demands served by each parking lot are shown in Table
Parking demands served by each parking lot.
Scenario | Vehicle type | Parking lot index | |||||
---|---|---|---|---|---|---|---|
8 | 9 | 10 | 11 | 12 | 13 | ||
1 | Intelligent | 278 | 71 | 109 | 9 | 7 | 107 |
Regular | 1055 | 462 | 153 | 210 | 275 | 157 | |
2 | Intelligent | 548 | 184 | 154 | 43 | 81 | 150 |
Regular | 785 | 350 | 108 | 174 | 201 | 115 | |
3 | Intelligent | 816 | 292 | 196 | 87 | 162 | 188 |
Regular | 517 | 243 | 67 | 127 | 120 | 78 | |
4 | Intelligent | 1078 | 397 | 232 | 149 | 232 | 229 |
Regular | 255 | 137 | 31 | 67 | 50 | 37 | |
5 | Intelligent | 1333 | 533 | 263 | 216 | 284 | 267 |
The performances of PRS are presented in Figures
Average travel time with the scenarios.
Average walking distance with the scenarios.
The average walking distances were obtained and are shown in Figure
With the wide ownership and usage of smart phones, PRS becomes practical to reduce the travel time in cruising for vacant spaces. This paper assesses the impact of PRS by introducing agent-based simulation to model the drivers’ responses to the parking information, as well as the processes of cruising for vacant spaces and making parking reservation decisions dynamically. The involved functionality components were treated as different agents, including vehicles, parking lots, network, and management center. Conclusions are drawn as follows: To describe the vehicles moving on the network dynamically, the processes of cruising for vacant spaces and making parking reservation decisions were analyzed. Vehicles were divided into two categories: the intelligent vehicles and the regular ones. Only the intelligent vehicles have the ability to make a parking reservation decision, while the regular ones have to cruise for vacant spaces. Agent-based simulation was introduced to describe the processes of cruising for vacant spaces and making reservation decisions dynamically. Of all involved components, only the vehicle agents can move dynamically. To reserve at least one space in the reserved parking lot, the rules of attaching/detaching were defined. If the reservation requests were accepted, the vehicles are attached to the reserved parking lot; otherwise, drivers make a parking choice decision according to the rule discussed. Moreover, if the intelligent vehicles arrive at the target parking lot, they have to be detached from the parking lot.
The simulation results indicate the average travel time increases with the improvement of the penetration rates for the regular vehicles. While the results are promising, further studies may be conducted to improve the performances of the proposed method. As a foundation of cruising for vacant spaces and making parking reservation decisions is the parking disutility function, it is critical to adjust the parameters to describe the processes accurately. Moreover, the parameters of the traffic flow model used in this study may be further investigated. With large amount of field data, agent-based simulation may be studied to evaluate the performances of PRS more practically. Further studies may implement the proposed algorithm in a real environment, and then the performances with more traffic data can be assessed.
Any opinions, findings, and conclusions or recommendations expressed in this paper are those of the authors and do not necessarily reflect the views of the sponsors.
The authors declare that there are no conflicts of interest regarding the publication of this paper.
The authors would like to express their appreciation to Professor Zhong-Ren Peng from Department of Urban and Regional Planning, University of Florida, for his valuable suggestions during the conduct of this study. Additionally, the support from the Humanities and Social Science Research Project, Ministry of Education, China (15YJCZH148), the Philosophy and Social Science Research Project of Shanghai, China (2014BGL009), and the Shanghai Municipal Natural Science Foundation (17ZR1445500) is greatly appreciated.