Turbo-Roundabouts as an Instrument for Improving the Efficiency and Safety in Urban Area: An Italian Case Study
Abstract
:1. Introduction
2. Literature Review
- macroscopic; treat stationary and aggregated values,
- mesoscopic; study the temporal evolution of all the variables and,
- microscopic; intermediate level between the two previously-mentioned levels.
- VISSIM, developed by Planung Transport Verkehr (PTV), a German company, is a microscopic simulation program for modeling multimodal transport operations. VISSIM is characterized by a discrete, stochastic, and time step-based model in which vehicle units are represented as individual entities.
- AIMSUN Next, developed by Siemens (one of the largest manufacturers of signal control systems), is capable of generating various traffic conditions based on either stochastic route choice or dynamic user equilibrium.
- PARAMICS, developed by Quadstone Limited, a Scottish company, is a software for modeling the movement and behavior of individual vehicles and transit on local and regional freeway networks.
- TRITONE, developed by University of Calabria, is an open-source platform that also allows the evaluation of traffic safety performance through a set of indicators that represent the interactions in real time between different pairs of vehicles of the traffic flow.
3. Materials and Methods
3.1. Site Selection
3.2. Traffic Scenarios
- Scenario n. 1: Q1 = 4594 vehicles/hour
- Scenario n. 2: Q2 = 4977 vehicles/hour
- Scenario n. 3: Q3 = 5359 vehicles/hour
- Scenario n. 4: Q4 = 5742 vehicles/hour
3.3. Micro-Simulation Process
- (1)
- AIMSUN Next for the calculation of the operational performance corresponding to the traffic scenarios described in the previous paragraph;
- (2)
- SSAM for the estimation of the safety indicators, starting from the kinematic parameters associated with all the vehicle trajectories obtained as output of the AIMSUN Next software.
3.3.1. AIMSUN Model
- (1)
- geometric scheme of the road network.
- (2)
- modelling of vehicle behavior.
- Centroids. They are the source of vehicles entering and exiting the network.
- Nodes (intersections). They are treated as node servers in the mesoscopic representation. In the node servers, vehicles are directed from one section to a turning and then to their next section. These turnings connect the lanes of the originating section to the lanes of the destination section. All vehicles are assumed to travel unimpeded, i.e., at free-flow speed, in turnings.
- Edges. They are the segments that connect the nodes. Each edge contains information about its geometry (e.g., the number of lanes, shoulder width, etc.).
- The behavioral models used in AIMSUN are the following:
- Behavioral models in edges: Car-following models and lane-changing models.
- Behavioral models in nodes (intersections): Gap acceptance and lane choice models.
- -
- t = simulation time;
- -
- n = vehicle number ordered by its arrival time on the lane;
- -
- x(t, n) = position of vehicle n at time t;
- -
- Sn = speed of the nth vehicle;
- -
- Sn(max) = maximum speed of the vehicle (the minimum between the desired maximum speed of the vehicle and the maximum speed of the edge);
- -
- EL = effective length of the vehicle (vehicle length plus minimum distance between vehicles);
- -
- d = distance between the vehicles;
- -
- RT = reaction time of the driver of the follower vehicle;
- -
- dc = maximum deceleration of the considered vehicle;
- -
- dc(e) = estimate of the desired deceleration of the leading vehicle;
- -
- d = distance between the vehicles.
- (1)
- Total number of stops: A stop for a vehicle happens whenever its speed decreases below the queue entry speed and while it remains below the queue exit speed parameter. Once the vehicle speed goes above the queue exit speed parameter the vehicle is no longer considered in a queue nor stopped. A new stop will be added to the number of stops statistics when the vehicle speed goes below queue entry speed again.
- (2)
- Delay Time: Average delay time per vehicle per kilometer (seconds/km). This is the difference between the expected travel time (the time it would take to traverse the system under ideal conditions) and the travel time. It is calculated as the average of all vehicles and then converted into time per kilometer. It does not include the time spent in a virtual queue.
3.3.2. SSAM Model
- -
- The Dimension class contains information about the spatial characteristics of the rectangular bounding box of the microsimulation environment.
- -
- The Timestep class contains a record of the current time step since the start of the simulation. This variable allows SSAM to position the vehicles in time.
- -
- The Vehicle class contains information about the spatial characteristics of the vehicle and the speed and acceleration values used to predict vehicle motion.
- -
- The Conflict class contains information about the conflict angle, which is an approximate angle for a hypothetical collision between colliding vehicles based on the estimated heading of each vehicle. Depending on the values of this angle, the resulting conflict may be a rear-end collision, a lane change, or crossing movement. Specifically, the rear-end angle is used to define a potential collision when following and lane changing, and the crossing angle defines potential collisions in head-on scenarios, such as maneuvering through an intersection (Figure 6).
- Maximum speed (MaxS)— the maximum speed (m/s) of two vehicles involved in the conflict event [64].
- Difference in vehicle speeds (DeltaS)—the absolute value of difference in speeds (m/s) of two conflicting vehicles [64].
- Initial Deceleration Rate (DR)—the magnitude of the deceleration action (m/s2) of a driver the moment he begins an evasive braking maneuver [65].
- Maximum deceleration rate (MaxD)—the maximum deceleration (m/s2) of the through vehicle [45].
4. Results and Discussion
- ➢
- Crossing conflicts: These types of conflicts almost cancel out in the turbo-roundabout configuration compared to the multi-lane roundabout configuration. It should be noted, however, that the percentage of these conflicts, as could logically be expected in a context where the two main intersections are roundabouts, is already low in each of the scenarios considered. Therefore, it is not considered appropriate to highlight this result.
- ➢
- Rear End Conflicts: Although this type of conflict generally has the least severe consequences, it is the most common in all of the traffic scenarios considered. In scenarios n. 3 and n. 4, which refer to the road configuration characterized by the presence of multi-lane roundabouts, the number of conflicts even exceeds 8000 on average. The presence of turbo-roundabouts drastically reduces these conflicts, especially in configurations with high traffic volumes: −38% (scenario n. 1), −42% (scenario n. 2), −68% (scenario n. 3), −69% (scenario n. 4).
- ➢
- Lane change Conflicts: The two multi-lane roundabouts have two lanes on the circulatory roadway. It was logical to expect that the simulations would yield a large number of lane change conflicts; nearly 450 in scenario n. 1, about 700 in scenario n. 2, and over 1200 in both scenario n. 3 and scenario n. 4. The design of turbo-roundabouts would result in a very significant reduction in these conflicts. This is confirmed by the following reductions identified in the simulations: −75% (scenario n. 1), −78% (scenario n. 2), −86% (scenario n. 3) and −86% (scenario n. 4).
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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VISSIM | AIMSUN Next | PARAMICS | TRITONE | |
---|---|---|---|---|
Advantages | ||||
User-defined algorithms for vehicle movement control. | X | X | ||
Appropriate for traffic simulation, traffic data analysis, planning, etc. | X | X | X | X |
Intersection type is not predefined. | X | |||
The duration of traffic analysis can be defined by the user. | X | |||
Includes psycho-physical model for car-following. | ||||
Includes car-following models and other calibration methods. | X | |||
Suitable for safety analysis based on vehicle trajectory. | X | X | X | |
Disadvantages | ||||
Developing a complete algorithm for safety analysis is difficult, especially for new users | X | |||
There are few options for modeling accidents. | X | |||
Coding the inputs and outputs is very time consuming and labor intensive. | X | X | ||
The modeled trajectories are not realistic. | X | |||
Traffic volume is determined using origin-destination matrices. | X |
Type of Turbo-Roundabout | ||||
---|---|---|---|---|
Design parameter | Mini | Standard | Medium | Large |
R1 | 10.45 m | 12.00 m | 14.95 m | 19.95 m |
R2 | 15.85 m | 17.15 m | 20.00 m | 24.90 m |
R3 | 16.15 m | 17.45 m | 20.30 m | 25.20 m |
R4 | 21.20 m | 22.45 m | 25.25 m | 29.95 m |
r1 | 10.95 m | 12.50 m | 15.45 m | 20.45 m |
r2 | 15.65 m | 16.95 m | 19.80 m | 24.70 m |
r3 | 16.35 m | 17.65 m | 20.50 m | 25.40 m |
r4 | 20.70 m | 21.95 m | 24.75 m | 29.45 m |
Ds | 0.30 m | 0.30 m | 0.30 m | 0.30 m |
ds | 0.70 m | 0.70 m | 0.70 m | 0.70 m |
Bi | 5.40 m | 5.15 m | 5.05 m | 4.95 m |
Be | 5.05 m | 5.00 m | 4.95 m | 4.75 m |
bi | 4.70 m | 4.45 m | 4.35 m | 4.25 m |
be | 4.35 m | 4.30 m | 4.25 m | 4.05 m |
Dv | 5.75 m | 5.30 m | 5.15 m | 5.15 m |
Du | 5.05 m | 5.00 m | 4.95 m | 4.75 m |
Total Number of Stops | Delay Time [s/km] | TTCmin [s] | PET [s] | Maximum Speed (MaxS) [m/s] | Initial Deceleration Rate (DR) [m/s2] | Total Number of Conflicts | Crossing Conflicts | Rear End Conflicts | Lane Change Conflicts | |
---|---|---|---|---|---|---|---|---|---|---|
Scenario n. 1 | 1113 | 24.75 | 0.93 | 1.85 | 6.44 | 2.84 | 3364 | 10 | 2911 | 443 |
Scenario n. 2 | 1313 | 35.89 | 0.91 | 1.91 | 5.82 | 2.87 | 4699 | 23 | 3982 | 694 |
Scenario n. 3 | 2819 | 74.34 | 0.88 | 1.84 | 4.67 | 2.51 | 9028 | 34 | 7783 | 1211 |
Scenario n. 4 | 3176 | 91.40 | 0.89 | 1.87 | 4.57 | 2.50 | 9683 | 45 | 8360 | 1278 |
Total Number of Stops | Delay Time [s/km] | TTCmin [s] | PET [s] | Maximum Speed (MaxS) [m/s] | Initial Deceleration Rate (DR) [m/s2] | Total Number of Conflicts | Crossing Conflicts | Rear End Conflicts | Lane Change Conflicts | |
---|---|---|---|---|---|---|---|---|---|---|
Scenario n. 1 | 1926 | 64.16 | 1.15 | 2.31 | 4.86 | 2.24 | 2009 | 1 | 1798 | 112 |
Scenario n. 2 | 1999 | 73.44 | 1.23 | 2.49 | 3.98 | 1.96 | 2417 | 2 | 2310 | 156 |
Scenario n. 3 | 2056 | 107.44 | 1.25 | 2.47 | 3.82 | 1.83 | 2659 | 2 | 2490 | 167 |
Scenario n. 4 | 2368 | 136.92 | 1.24 | 2.43 | 3.74 | 1.73 | 2741 | 4 | 2567 | 183 |
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Leonardi, S.; Distefano, N. Turbo-Roundabouts as an Instrument for Improving the Efficiency and Safety in Urban Area: An Italian Case Study. Sustainability 2023, 15, 3223. https://doi.org/10.3390/su15043223
Leonardi S, Distefano N. Turbo-Roundabouts as an Instrument for Improving the Efficiency and Safety in Urban Area: An Italian Case Study. Sustainability. 2023; 15(4):3223. https://doi.org/10.3390/su15043223
Chicago/Turabian StyleLeonardi, Salvatore, and Natalia Distefano. 2023. "Turbo-Roundabouts as an Instrument for Improving the Efficiency and Safety in Urban Area: An Italian Case Study" Sustainability 15, no. 4: 3223. https://doi.org/10.3390/su15043223
APA StyleLeonardi, S., & Distefano, N. (2023). Turbo-Roundabouts as an Instrument for Improving the Efficiency and Safety in Urban Area: An Italian Case Study. Sustainability, 15(4), 3223. https://doi.org/10.3390/su15043223