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A microscopic traffic flow model on network with destination-aware V2V communications and rational decision-making

Emiliano Cristiani, Francesca L. Ignoto

TL;DR

This work develops a microscopic traffic flow framework on networks in which vehicles equipped with V2V communications share destinations and planned routes to inform rational, time-minimizing decisions. It extends standard Reactive and Dynamic User Equilibria by incorporating partial information and nowcasting via finite-range communications, and analyzes how information exchange reshapes route choices and network performance. Through BB, RUE, DUE baselines and two V2V-augmented models (V2V-RUE and V2V-DUE), the study reveals phenomena such as nonmonotone improvements with communication range and the emergence of novel equilibria, validated by numerous simulations on simple and Manhattan-like networks. The results connect microscopic agent dynamics with equilibrium concepts and motivate future work on macroscopic limits and mean-field-type analyses of V2V-enabled traffic systems.

Abstract

In this paper we carry out a computational study of a novel microscopic follow-the-leader model for traffic flow on road networks. We assume that each driver has its own origin and destination, and wants to complete its journey in minimal time. We also assume that each driver is able to take rational decisions at junctions and can change route while moving depending on the traffic conditions. The main novelty of the model is that vehicles can automatically and anonymously share information about their position, destination, and planned path when they are close to each other within a certain distance. The pieces of information acquired during the journey are used to optimize the route itself. In the limit case of an infinite communication range, we recover the classical Reactive User Equilibrium and Dynamic User Equilibrium.

A microscopic traffic flow model on network with destination-aware V2V communications and rational decision-making

TL;DR

This work develops a microscopic traffic flow framework on networks in which vehicles equipped with V2V communications share destinations and planned routes to inform rational, time-minimizing decisions. It extends standard Reactive and Dynamic User Equilibria by incorporating partial information and nowcasting via finite-range communications, and analyzes how information exchange reshapes route choices and network performance. Through BB, RUE, DUE baselines and two V2V-augmented models (V2V-RUE and V2V-DUE), the study reveals phenomena such as nonmonotone improvements with communication range and the emergence of novel equilibria, validated by numerous simulations on simple and Manhattan-like networks. The results connect microscopic agent dynamics with equilibrium concepts and motivate future work on macroscopic limits and mean-field-type analyses of V2V-enabled traffic systems.

Abstract

In this paper we carry out a computational study of a novel microscopic follow-the-leader model for traffic flow on road networks. We assume that each driver has its own origin and destination, and wants to complete its journey in minimal time. We also assume that each driver is able to take rational decisions at junctions and can change route while moving depending on the traffic conditions. The main novelty of the model is that vehicles can automatically and anonymously share information about their position, destination, and planned path when they are close to each other within a certain distance. The pieces of information acquired during the journey are used to optimize the route itself. In the limit case of an infinite communication range, we recover the classical Reactive User Equilibrium and Dynamic User Equilibrium.

Paper Structure

This paper contains 34 sections, 3 theorems, 18 equations, 8 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Context and relevant literature
  3. Main contribution
  4. Paper organization
  5. The models with full information
  6. Notations.
  7. Considered networks
  8. Dynamic network loading
  9. Static route optimization via Dynamic Programming Principle
  10. Reactive user equilibrium
  11. Dynamic user equilibrium
  12. Numerical tests
  13. Test 0: Basic statistics
  14. Test 1: BB vs. RUE vs. DUE
  15. The models with V2V communications
  16. ...and 19 more sections

Key Result

Lemma 1

Sample lemma text in italic, except for function names in upright : $\sin$, sign($\cdot$), rank($\cdot$), etc.

Figures (8)

  • Figure 1: Networks utilized for numerical tests, with road and junction numbering. Roads are all one-way, their colors indicate the direction of motion (blue=up, brown=down, red=right, green=left). Roads between the same pair of junctions are actually superimposed, the shift is only for pictorial purposes. Cars are visualized as colored dots
  • Figure 2: Test 0. Cumulative average of $\textsc{ttt}$ obtained for RUE, with $L_\textsc{r}=50$ and random OD pairs
  • Figure 3: Test 1. Three screenshots, corresponding to the same time $t$, of the simulation on the 11-road simple network (colors are just pictorial). They clearly show the difference among the three degrees of rationality
  • Figure 4: Test 1. Comparison between average $\textsc{ttt}$'s obtained with 25, 50, 75, and 100 cars and different degrees of rationality (BB, RUE, DUE)
  • Figure 5: Test 2. Spreading of information
  • ...and 3 more figures

Theorems & Definitions (10)

  • Remark 1: Match between forward and backward dynamics
  • Remark 2: Nowcast inaccuracy
  • Remark 3
  • Lemma 1
  • proof
  • Theorem 1
  • Proposition 1
  • Remark 4
  • Example 1
  • Definition 1