Introduction
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We propose a new pheromone model, called the inverted pheromone model, including occupancy time and fuel consumption rate pheromones. It gives negative feedback to the congested road segments. The pheromone-based traffic routing method minimizes travel time, fuel consumption, and emissions by avoiding congested routes and finding alternative routes with more environmentally friendly.
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We investigate the cooperative negotiation between vehicles in traffic routing to enhance traffic efficiency further. Vehicles collaborate when they are near, they effectively spread themselves out among different routes, preventing traffic jams on the same paths.
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Extensive simulations based on the Simulation of Urban Mobility (SUMO) simulator were conducted to demonstrate the effectiveness of the proposed framework under various traffic conditions. The results of simulations show that the system greatly increases traffic efficiency.
Background and related work
Decentralized intelligent transportation systems
Traffic routing optimization
ACO-based traffic routing method with automated negotiation
Transportation model
Dynamic traffic routing problem
Inverted pheromone-based traffic routing
Cooperative negotiation-based traffic routing
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\(|P_k |\) represents the number alternative paths of vehicle \(v_k\),
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\(f_{G}(p^m_k)\) denotes the global cost of selecting \(p^m_k\), which is the average of estimated travel times of different paths.
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\(f_{L}(p^m_k)\) denotes the local cost of selecting \(p^m_k\), which is the estimated travel time of the path \(p^m_k\).
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\(\omega _1\) and \(\omega _2\) parameters (with \(\omega _1,\omega _2 \in \{0,1\}\) and \(\omega _1+\omega _2=1\)) represent the preferences of vehicles for global cost and local cost, respectively. A vehicle with \(\omega _1 = 1\) is an altruistic agent that reduces all vehicles’ total cost or average journey time. In contrast, a vehicle with \(\omega _2 = 1\) prioritizes minimizing its local cost or travel time.
Performance evaluation
Simulation settings
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Shortest Time Routing Method (ShortRoute): Vehicles follow the shortest path based on the free-flow travel time determined by the route’s length and speed limit, ignoring road capacity and traffic density.
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Inverted Pheromone-based Routing Method (IPheRoute): The routing method is based on the inverted pheromone model, which includes occupancy time and fuel consumption information.
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Inverted Pheromone-based Routing with Cooperative Negotiation Method (IPheNegoRoute): This is the proposed routing method, which integrates the inverted pheromone model, the cooperative negotiation technique, and the traffic density-based congestion detection mechanism. All vehicles cooperate to minimize the global cost (i.e., \(\omega _1 = 1\) and \(\omega _2 = 0\) for all vehicles).
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Dynamic Traffic Assignment Method (DTA) [28, 29]: Through an iterative simulation procedure, the method can approximately achieve user equilibrium. However, it is not suited for real-time and practical route recommendations due to its substantial computational overhead and requirement for a complete understanding of the traffic system. Nevertheless, it can provide the optimal routing results for comparison in a small-scale traffic scenario. The DTA tool in SUMO is used with default settings.
Parameters | Value |
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Simulator | SUMO 1.12.0 |
Simulation steps | 3600 s |
Road networks | Yeouido, Gangnam areas |
Vehicle size | 5 m |
Vehicle gap | 2.5 m |
Vehicle emission class | HBEFA3/PC_G_EU4 |
Fuel consumption model | EMIT |
Congestion threshold value | \(\phi = 0.7\) |
Pheromone evaporation rate | \(\rho = 0.5\) |
Results and discussion
Yeouido island traffic network
Method | Traffic demand (veh/h) | ||
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2500 | 3500 | 4000 | |
Average travel time (s) | |||
ShortRoute | 119.48 ± 3.49 | 147.08 ± 3.66 | 170.40 ± 3.34 |
PheRoute | 117.32 ± 1.30 | 136.61 ± 1.06 | 146.24 ± 3.56 |
IPheRoute | 115.97 ± 2.74 | 130.19 ± 1.12 | 137.74 ± 2.22 |
IPheNegoRoute | 113.88 ± 1.24 | 126.82 ± 2.57 | 133.10 ± 3.77 |
DTA | 107.24 ± 1.79 | 118.80 ± 3.30 | 123.88 ± 1.90 |
Average fuel consumption (ml) | |||
ShortRoute | 247.66 ± 3.59 | 284.06 ± 5.18 | 301.39 ± 6.55 |
PheRoute | 249.88 ± 2.49 | 276.03 ± 4.36 | 283.96 ± 6.02 |
IPheRoute | 248.23 ± 2.65 | 268.91 ± 5.81 | 275.99 ± 6.83 |
IPheNegoRoute | 245.18 ± 2.92 | 266.11 ± 4.10 | 271.09 ± 5.50 |
DTA | 244.54 ± 2.58 | 257.35 ± 4.55 | 265.81 ± 5.49 |
Method | Traffic demand (veh/h) | ||
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4000 | 5000 | 5500 | |
Average travel time (s) | |||
ShortRoute | 142.14 ± 5.87 | 174.27 ± 5.34 | 224.54 ± 7.21 |
PheRoute | 137.79 ± 13.10 | 162.15 ± 7.00 | 200.11 ± 11.50 |
IPheRoute | 136.13 ± 14.97 | 161.47 ± 7.96 | 183.82 ± 11.61 |
IPheNegoRoute | 133.51 ± 10.41 | 154.04 ± 6.29 | 167.46 ± 9.82 |
Average fuel consumption (ml) | |||
ShortRoute | 311.94 ± 8.85 | 347.23 ± 13.12 | 392.00 ± 10.05 |
PheRoute | 307.78 ± 17.96 | 330.89 ± 7.47 | 370.62 ± 13.10 |
IPheRoute | 306.45 ± 20.46 | 330.43 ± 7.11 | 352.47 ± 14.09 |
IPheNegoRoute | 303.00 ± 14.61 | 323.91 ± 10.00 | 336.73 ± 12.99 |