The rapid increase in vehicle density on roads owing to urbanization and motorization has led to increased risks of roadblocks, traffic jams, and accidents. To ensure the reliability of transportation, it is crucial to have stable and timely transmission of safety messages through Vehicle Ad-hoc Networks (VANETs). However, frequent vehicle movement and changes in the network topology may cause link breakage and packet loss. This paper proposes a solution that uses a fuzzy logic system in both the Medium Access Control (MAC) layer and the network layer to broadcast safety messages efficiently. The proposed rule-based model optimizes the Contention Window (CW) and relay selection process to adapt to different traffic conditions. The dynamic CW MAC (DYCW-MAC) model selects the optimum size of CW based on network parameters such as density, velocity, and link quality factor. For multi-hop communication, the model determines the next forwarding relay by considering factors such as direction, velocity difference, coverage factor, and Fast-Expected Transmission Count (F-ETX) between the sender vehicle and surrounding vehicles within its transmission range. The simulation results indicate that the DYCW-MAC model enhances the network throughput and decreases the average packet delay in comparison to other models. On average, the proposed model has a 28% lower throughput standard deviation than other comparable models considered.

Optimization of Contention Window and Relay Selection for Efficient Routing Using Fuzzy Logic Model

In densely populated cities, severe vehicle congestion is an obstacle due to the massive increase of vehicles and bottleneck of roads. Traffic congestion brings severe economic and environmental hazards. Although it is hard to eliminate, it can be mitigated by finding the occurrence of the congestion well in advance to initiate appropriate control measures. Accurately estimating traffic speed, travel time, and other external factors is mandatory for forecasting congestion levels in a dynamic road network. In recent years, sensor-based traffic detection technologies have evolved to achieve accuracy and safety in intelligent transportation systems (ITS). However, some of them need more reliability, have weak usage in different visibility conditions, are hard to set, and have a high cost. Alternatively, modern navigation systems are developed to find the fastest, alternative routes, travel times, and congestion levels for a given source to destination. These systems use Global Positioning Systems (GPS) data to provide the routing information. Due to inaccurate/ inadequate GPS data samples, sometimes these systems may give misleading information. This work presents Long-Range Wide Area Network (LoRaWAN) architecture to overcome the abovementioned limitations. In our proposed case study, we have used the Dragino LoRaWAn Gps sensor (IN865), air quality sensor (MQ135), temperature and humidity sensor (SHT31), and other sensors to extract direct and indirect factors that influence traffic congestion. Haversine formula and hyper-heuristic-based Encoder-Decoder using Gated Recurrent Units with attention mechanism (ED-GRUAT) are used to estimate the segment-based distance, speed, vehicle travel times, and congestion levels. Our case study helps to calculate accurate traffic speed, travel times, and congestion levels.

A Novel LoRaWAn-based Real-time Traffic Analysis Approach for Vehicle Congestion Estimation

In densely populated cities, severe vehicle congestion is an obstacle due to the massive increase of vehicles and bottleneck of roads. Traffic congestion brings severe economic and environmental hazards. Although it is hard to eliminate, it can be mitigated by finding the occurrence of the congestion well in advance to initiate appropriate control measures. Accurately estimating traffic speed, travel time, and other external factors is mandatory for forecasting congestion levels in a dynamic road network. In recent years, sensor-based traffic detection technologies have evolved to achieve accuracy and safety in intelligent transportation systems (ITS). However, some of them need more reliability, have weak usage in different visibility conditions, are hard to set, and have a high cost. Alternatively, modern navigation systems are developed to find the fastest, alternative routes, travel times, and congestion levels for a given source to destination. These systems use Global Positioning Systems (GPS) data to provide the routing information. Due to inaccurate/ inadequate GPS data samples, sometimes these systems may give misleading information. This work presents Long-Range Wide Area Network (LoRaWAN) architecture to overcome the abovementioned limitations. In our proposed case study, we have used the Dragino LoRaWAn Gps sensor (IN865), air quality sensor (MQ135), temperature and humidity sensor (SHT31), and other sensors to extract direct and indirect factors that influence traffic congestion. Haversine formula and hyper-heuristic-based Encoder-Decoder using Gated Recurrent Units with attention mechanism (ED-GRUAT) are used to estimate the segment-based distance, speed, vehicle travel times, and congestion levels. Our case study helps to calculate accurate traffic speed, travel times, and congestion levels. 

Congestion control is a widely accepted domain in Intelligent Transportation System. Two approaches are commonly used to address the issue: either by controlling the traffic signals or by re-routing the vehicles in a congested state. However, the objective is to minimize the average travel time of the vehicles in a given road scenario. Choosing shortest path could be a solution. But the vehicles, following the shortest path, may face congestion if the decision is done in an un-coordinated manner. This could be due to non-inclusion of crucial decision parameter(s) and lack of cooperative decision on the decisive parameters of the concerned traffic scenario. There are efforts to include the density of the road segments within decision variables. The novelty of the proposed solution is to address the adaptive nature of the density parameter and considers effectively in the solution proposal. The solution considers the effect of density in a nearby road segment is more than the rare one. The introduction of the adaptive nature of this decision variable models the real road network more accurately and subsequent solution is more effective. Exhaustive experimentation has been done, considering various use cases. The proposed Density Based Coordinated Vehicle Rerouting, coined as “DB-Corouting” algorithm is simulated through “SUMO” and “Open Street Map” and the necessary finding ensures the effectiveness of the proposed solution in terms of selected metrics such as average traveling time, average waiting time, Traffic satisfaction Index etc.. The proposed solution outperforms the comparable solutions in terms of the selected metrics and always offers an efficient solution irrespective of traffic distribution.

Pratik Dutta

Papers

DB-Corouting: Density Based Coordinated Vehicle Rerouting in Smart Environment