An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space
Abstract
:1. Introduction
2. Background
3. Model Description
3.1. Purpose
3.2. Variables
- The vehicle mass parameter, drawn from a random uniform distribution between 1000 and 3000 kg (inclusive), allows the model to simulate a wider variety of vehicle types, from sedans to SUVs and hatchback. The rationale behind this distribution was to try intersect the EV and ICEV vehicle types, which larger vehicles such as vans or trucks are not part of; the model distributes vehicles arbitrarily across the environment with varying weights (source [39]).
- The top speed measure is between 30 and 45 mph (48, 72 km/h) and is only applied to vehicles that do not adhere to speed limits. This measure is applied only if Speed Adherence is ≥1 (source [40]).
- The gap acceptance parameter can be between 1 to 10 for each vehicle. The variable assigns a distance between two vehicles in meters. This ensures a wider variety of visual impairment is captured as some people with healthier eyes keep a fair distance from vehicles in front usually adhering to the 2-s rule compared to people with worse vision. Furthermore, the distance had to be relative to the average road distance in the model.
- The number of vehicles generated in the model, N. This can be between 1 and 500. However, this can be adjusted depending on the compute power accessible. The hardware accessible at the time of writing this article could only efficiently simulate up to 500 vehicles in 3D space while yielding valid results (refer to the Conclusions section for more on this limitation).
- The speed adherence variable can be between . This quantifies the proportion of vehicles that will not adhere to the speed limits applied to the road they are driving on.
- The urban road network consists of 1295 roads which vehicles drive on and 354 intersections which consist of traffic rules (Algorithm 1, Ref. [28]). The road network has been designed to depict a small urban town.
3.3. Model Overview
3.4. Agent
3.5. Environment
- (A) A two-way local road with a speed limit of 20 mph (32 km/h);
- (B) A two-way corner road with a speed limit of 10 mph (16 km/h);
- (C) A two-way fixed road with a speed limit of 30 mph (48 km/h);
- (D) An eight-way intersection. Right-of-way is for traffic on horizontal lanes, and the speed limit is 10 mph (16 km/h);
- (E) A two-way t-junction. Right-of-way is for horizontal lanes, and the speed limit is 10 mph (16 km/h).
4. Results
- That it produces outputs at a time resolution of 15 min; our model, on the other hand, has a time resolution of 1 s. This way, we can capture finer detail such as the impact of traffic lights on acceleration/deceleration and momentary traffic congestion;
- That it only captures trips that are split into commuters and non-commuters. Thus, the modelled scenarios revolve around two profiles of drivers. Our model manipulates the entire system from the street network to traffic rules and vehicles; thus, no single driver profile is modelled, but a heterogeneous set of behaviours are captured.
4.1. Electric Energy Consumption Calculation
4.2. Experiments
4.2.1. Fuel Consumption of Internal Combustion Engine Vehicle Fleet
4.2.2. Monetary Costs of Fuel and Electric Consumption
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix A.1. Energy Calculation
- F is the force provided by the engine driving the vehicle forward (N);
- m is the mass of the vehicle (kg);
- g is the gravitational acceleration (m/s2);
- is the angle of the surface on which the vehicle is driving on;
- is the density of air (1.225 kg/m3);
- is the drag coefficient;
- A is the reference area of the vehicle (m2) (width × height);
- v is the velocity (m/s), and;
- is the coefficient of rolling resistance.
Appendix B
Tables
Variable | Output Type |
---|---|
VelocityChange | Float |
Acceleration | Float |
Deceleration | Float |
Braking Energy (kWh) 1 | Float |
Drag_Force | Float |
Acceleration_Force | Float |
Total_Force | Float |
Drag_Work | Float |
Acceleration_Work | Float |
Total_Work | Float |
Energy_Input (kWh) | Float |
Energy_Input_Sum (kWh) | Float |
MC | PHEV | ICEV |
---|---|---|
1 | 0.60 | 0.92 |
2 | 0.43 | 0.56 |
3 | 0.22 | 0.34 |
4 | 1.93 | 4.42 |
5 | 1.50 | 2.25 |
6 | 0.67 | 1.22 |
7 | 2.79 | 5.66 |
8 | 1.72 | 3.71 |
9 | 1.12 | 1.98 |
Appendix C
Appendix C.1. Figures
References
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Entity | Parameter | Values |
---|---|---|
Vehicle | Mass | [1000, 3000] (kg) |
Top speed | [30, 45] (mph), [48, 72] (km/h) | |
Gap acceptance | [1, 10] (m) | |
Environment | N. of vehicles | [1, 500] |
Speed adherence | [0, N] | |
Roads | 1295 | |
Intersections | 354 |
Variable | Output Type |
---|---|
AgentID | Integer |
xAxisPos | Float |
zAxisPos | Float |
collisions | Integer |
topSpeed (mph) | Float |
currentSpeed (mph) | Float |
distanceOfTravel (meters) | Float |
gapAcceptance (raycastLength) | Integer |
tractionControl | Bool |
velocityMagnitude | Float |
vehicleMass | Integer |
downforce | Float |
date-time | DateTime |
Parameter | Value |
---|---|
Height (m) | 1.71 |
Width (m) | 1.80 |
k | 65% (source [56]) 1 |
m (kg) | 1925 |
0.33 |
Variable | Low Adherence | Medium Adherence | High Adherence |
---|---|---|---|
Low Density | Condition 1, 10 vehicles, 10 non-adherence | Condition 2, 10 vehicles, 5 non-adherence | Condition 3, 10 vehicles, 0 non-adherence |
Mid Density | Condition 4, 50 vehicles, 50 non-adherence | Condition 5, 50 vehicles, 25 non-adherence | Condition 6, 50 vehicles, 0 non-adherence |
High Density | Condition 7, 100 vehicles, 100 non-adherence | Condition 8, 100 vehicles, 50 non-adherence | Condition 9, 100 vehicles, 0 non-adherence |
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Olmez, S.; Thompson, J.; Marfleet, E.; Suchak, K.; Heppenstall, A.; Manley, E.; Whipp, A.; Vidanaarachchi, R. An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space. Energies 2022, 15, 4031. https://doi.org/10.3390/en15114031
Olmez S, Thompson J, Marfleet E, Suchak K, Heppenstall A, Manley E, Whipp A, Vidanaarachchi R. An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space. Energies. 2022; 15(11):4031. https://doi.org/10.3390/en15114031
Chicago/Turabian StyleOlmez, Sedar, Jason Thompson, Ellie Marfleet, Keiran Suchak, Alison Heppenstall, Ed Manley, Annabel Whipp, and Rajith Vidanaarachchi. 2022. "An Agent-Based Model of Heterogeneous Driver Behaviour and Its Impact on Energy Consumption and Costs in Urban Space" Energies 15, no. 11: 4031. https://doi.org/10.3390/en15114031