# T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities

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## Abstract

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## 1. Introduction

- We propose a traffic control system that is based on road reservations, and therefore the proposed system ensures that the congestion never takes place;
- T-Coin is based on reward and punishment to encourage the drivers to take alternative paths that could alleviate traffic congestion;
- The path reservation can be traded among vehicles through a tender process, which prioritizes urgent path requests;
- A dynamic pricing model based on road length, road importance, and current traffic congestion is proposed.

## 2. Related Works

## 3. System Policies

#### 3.1. Vehicular Navigation Architecture

**Traffic Control Center (TCC)**TCC is a server that manages the path reservation process. It maintains the road reservation matrix (RRM) that contains the information about the number of reserved, as well as free, positions in each road segment during different time slots. The TCC updates traffic statistics such as average speed per road segment and vehicle arrival rate and is also responsible for computing road pricing and managing T-Coin transactions between vehicles in the case of the tender process. For a large-scale road network, a single TCC is not able to maintain the traffic updates for such a huge number of vehicles. To solve this scalability problem, the road network could be segmented into multiple regions, where each region is managed by its own TCC, and the TCCs communicate with each other to resolve cross regional paths. The design of the TCC server is left as future work.**Roadside Unit (RSU)**An RSU is a wireless gateway that connects the vehicles to the wired network (i.e., the Internet). The RSU and the vehicles communicate through dedicated short-range communication (DSRC). Generally, RSUs are deployed near the intersection and they are connected to each other using a wired network. RSUs are considered to be the backbone of the communication network that connects the wirelessly connected vehicles to the TCC.**eNodeB**In the case of RSU failure or disconnectivity between the RSU and the vehicle due to the DSRC range limit, the vehicles could also connect to the TCC through eNodeB which is a base station that connects the vehicle to the 4 G-LTE cellular network. It enables the vehicles to access the TCC in a ubiquitous way.**Vehicles**To communicate with RSUs and eNodeBs, all the vehicles are equipped with a DSRC communication device embedded in their on-board unit (OBU). They are also equipped with a GPS-based navigation system that has a digital roadmap. The Vehicles report the updates about their travel experience in road segments and at intersections along their travel path to the traffic control center. Since the travel paths are very sensitive information, all the communications between vehicles and the server are encrypted.

#### 3.2. T-Coin Balance

#### 3.3. Road Reservation Policy

#### 3.4. Reward and Punishment Policy

#### 3.5. Traffic Tender

#### 3.6. Misbehaviors Punishment

## 4. System Model

#### 4.1. Map Modeling

**I**be the set of road segment intersections. While $\mathit{V}$ denote the set of vehicles in the city map. The road segments are bidirectional with one lane or more in each direction.

#### 4.2. Traffic Flow and Travel Delay

#### 4.3. Path Reservation Process

#### 4.3.1. Road Reservation Matrix

#### 4.3.2. Traffic Quota Management

Algorithm 1. Congestion aware traffic quota allocation. |

1: $\sigma =\lceil {C}_{i,j}/100\rceil $ |

2: if $({M}_{{r}_{i,j},t}<{Q}_{i,j}^{T})$ then |

3: $\delta \leftarrow ({M}_{{r}_{i,j},t}-{Q}_{i,j}^{T})$ |

4: ${Q}_{i,j}^{c}\left(t+1\right)\leftarrow {Q}_{i,j}^{c}\left(t\right)+\delta $ |

5: else |

6: if (${Q}_{i,j}^{c}\left(t\right)<{Q}_{i,j}^{T}$) then |

7: ${Q}_{i,j}^{c}\left(t+1\right)\leftarrow {Q}_{i,j}^{c}\left(t\right)-\sigma $ |

8: end if |

9: end if |

#### 4.4. Dynamic Congestion Pricing

#### 4.4.1. Punishment Pricing

#### 4.4.2. Reward Pricing

## 5. Performance Evaluation

#### 5.1. Baselines

**Dijkstra Fixed T-Coin (DFT)**Since Dijkstra shortest path algorithm as a most intuitive path assignment method, we implement a system (hereafter referred to as DFT) that uses the Dijkstra algorithm to determine the shortest path from the starting point to the destination point. The vehicle will go through that path regardless of the traffic conditions within the roads of the path, where each road segment has a fixed traffic quota.

**Fixed Pricing T-Coin (FP T-Coin)**To test the usefulness of adaptive pricing and quota allotment functions of the proposed system, we have implemented a static version of the proposed system (hereafter abbreviated as FP T-Coin) and make it as a baseline, in which we make the paid traffic quota as a fixed amount, thus, the paid quota will remain the same regardless of the traffic congestion.

**Adaptive Pricing T-Coin (AP T-Coin)**This is the proposed system (hereafter abbreviated as AP T-Coin).

#### 5.2. Performance Metrics

**Additional Distance (traveled distance/shortest distance)**This metric is used to measure the additional distance that the vehicle has traveled as compared with the distance of its shortest path from the starting point to the destination point. In other words, we compute the ratio of the actual traveled distance from the starting point to the destination point as compared with the distance from the starting point to the destination point using the shortest path. We denote the additional distance of the vehicle ${v}_{i}$ as $AD\left({v}_{i}\right)$, see Equation (10), and the total additional distance of all vehicles is computed in Equation (11).

**Additional Time (traveling time/estimated traveling time)**This metric is used to measure the additional time that the vehicles have consumed in order to reach their final destination as compared with the initial estimated time of the shortest path. In other words, we compute the ratio of the actual traveling time from the starting point to the destination point, and the traveling time starting point to the destination point using the shortest path. We denote the additional time of vehicle ${v}_{i}$ as $AT\left({v}_{i}\right)$, and it can be computed as in Equation (12), and the total additional time of all vehicles is computed as in Equation (13).

**T-Coin Gain**This metric is used to measure the total gained T-Coin, in which we compute the total gained T-Coin from paid traffic punishments and the total lost T-Coin that was given as a reward for alleviating congestion, see Equation (14):

#### 5.3. Evolution Parameters

#### 5.4. Simulation Environment

^{2}was extracted by specifying the GPS coordinates of the desired area. The road network was extracted from the raw map using the simulation of urban mobility platform SUMO [29], see Figure 4. The proposed system was simulated using Omnet++ network simulator by implementing vehicular behaviors with the vehicular framework Veins [30]. The simulation parameters are depicted in Table 2. During the simulation, three modules ran simultaneously. Veins was responsible for the network simulation, while SUMO ran the road traffic simulation and sumo-launched acted as a broker between the two modules. SUMO dynamically updates the traffic-related values through the Traci interface, as shown in Figure 5.

## 6. Results Discussion

## 7. Conclusions

- In this work, we assumed the static management of the traffic lights, incorporating a dynamic traffic lights management system with T-Coin is one of our future directions.
- The proposed system has been proven to be efficiency in alleviating traffic congestion, however, the vehicle’s path represents very private information, if disclosed by a malicious node during the communication between the vehicle and the traffic control center. Therefore, an in-depth study of the security and privacy of the T-Coin system is one of our future directions.
- In the proposed system, the traffic control system is considered to be a centralized server. Changing the server model to a distributed vehicular server, where the server’s computational responsibility is performed by the vehicles themselves, is one of our future directions.

## Author Contributions

## Funding

## Conflicts of Interest

## References

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System | T-Coin | DIFTOS | SAINT | DIVERT | |
---|---|---|---|---|---|

Feature | |||||

Traffic Control Center | Centralized server | Hierarchal distributed vehicular servers | Centralized server | Distributed servers | |

Road reservation strategy | First come first served + Tender process | First come first served | First come first served | First come first served | |

Communication infrastructure dependency (V2I+4G) | Infrastructure required | Infrastructure not required | Infrastructure required | Infrastructure partially required | |

Rerouting strategy | Yes | Yes | Yes | Yes | |

Quota allocation | Yes | Yes | No | No | |

Driver decides rerouting path | Yes | No | No | No | |

Virtual currency | Yes | No | No | No | |

Destination-aware rerouting prioritization | Yes | No | No | No |

Parameters | Description |
---|---|

Network Simulator | Omnet++5 |

Traffic Simulator | Sumo 0.27.1 |

Map Information | OpenStreetMap |

Simulated Location | Beijing |

Simulated area | $4.5\mathrm{k}{\mathrm{m}}^{2}$ |

Parameter | Value |
---|---|

PHY model | 802.11 p |

Channel frequency | 5.890e9 Hz |

Propagation model | Two ray |

MAC model | EDCA |

Propagation distance | 450 m |

Maximum hop | 15 |

Fading model | Jakes model rayleigh fading |

Shadowing model | LogNormal |

Antenna model | Omnidirectional |

Transmission power | 20 mW |

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**MDPI and ACS Style**

Aung, N.; Zhang, W.; Dhelim, S.; Ai, Y. T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities. *Information* **2020**, *11*, 149.
https://doi.org/10.3390/info11030149

**AMA Style**

Aung N, Zhang W, Dhelim S, Ai Y. T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities. *Information*. 2020; 11(3):149.
https://doi.org/10.3390/info11030149

**Chicago/Turabian Style**

Aung, Nyothiri, Weidong Zhang, Sahraoui Dhelim, and Yibo Ai. 2020. "T-Coin: Dynamic Traffic Congestion Pricing System for the Internet of Vehicles in Smart Cities" *Information* 11, no. 3: 149.
https://doi.org/10.3390/info11030149