A Dynamic Programming Approach for Road Traffic Estimation
We consider a road network represented by a directed graph. We assume to collect many measurements of traffic flows on all the network arcs, or on a subset of them. We assume that the users are divided into different groups. Each group follows a different path. The flows of all user groups are modeled as a set of independent Poisson processes. Our focus is estimating the paths followed by each user group, and the means of the associated Poisson processes. We present a possible solution based on a Dynamic Programming algorithm. The method relies on the knowledge of high order cumulants. We discuss the theoretical properties of the introduced method. Finally, we present some numerical tests on well-known benchmark networks, using synthetic data.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning Traffic Speed Dynamics from Visualizations
Space-time visualizations of macroscopic or microscopic traffic variables is a qualitative tool used by traffic engineers to understand and analyze different aspects of road traffic dynamics. We present a deep learning m…
State EstimationBridging Traffic State and Trajectory for Dynamic Road Network and Trajectory Representation Learning
Effective urban traffic management is vital for sustainable city development, relying on intelligent systems with machine learning tasks such as traffic flow prediction and travel time estimation. Traditional approaches …
Graph AttentionRepresentation LearningTravel Time EstimationClustering of Urban Traffic Patterns by K-Means and Dynamic Time Warping: Case Study
Clustering of urban traffic patterns is an essential task in many different areas of traffic management and planning. In this paper, two significant applications in the clustering of urban traffic patterns are described.…
ClusteringDynamic Time WarpingManagementTime Series+1Conservation-Based Modeling and Boundary Control of Congestion with an Application to Traffic Management in Center City Philadelphia
This paper develops a conservation-based approach to model traffic dynamics and alleviate traffic congestion in a network of interconnected roads (NOIR). We generate a NOIR by using the Simulation of Urban Mobility (SUMO…
ManagementModel Predictive ControlBoundary Control of Traffic Congestion Modeled as a Non-stationary Stochastic Process
In this paper, we introduce a new conservation-based approach to model traffic dynamics, and apply the model predictive control (MPC) approach to control the boundary traffic inflow and outflow, so that the traffic conge…
ManagementModel Predictive Control