Learn, Assign, and Search: Real-Time Estimation of Dynamic Origin-Destination Flows Using Machine Learning Algorithms

Abstract

A common way to estimate dynamic origin-destination (O-D) flows is to establish and solve a bilevel optimization model. Though numerous efforts have been devoted to effectively and efficiently solving the model, challenges still exist because of the interdependence of jointly solving the upper level O-D estimation and lower level traffic assignment problems and the nonconvexity of the model. This paper presents an alternative framework for estimating dynamic O-D flows using machine learning algorithms. The framework consists of three major modules: a learner that learns the dynamic mapping patterns describing the relationship between prior O-D flows and observed link flows, an assigner that assigns a given O-D matrix to different links based on the learner, and a searcher that iteratively searches the optimal O-D solution using the assigner. A convolutional neural network is designed as the learner and trained as the assigner. Next, the algorithms to estimate a regular O-D matrix and real-time O-D flows are separately developed by using the assigner and two designed genetic algorithms built as the searcher. The framework was evaluated with a realistic network in the downtown area of Kunshan, China. The experimental studies show that the framework can achieve satisfactory estimation performances in real time. Meanwhile, it takes raw flow ranges as the prior inputs, making it robust in the case of lacking an accurate target O-D matrix. INDEX TERMS Dynamic O-D estimation, bilevel optimization, convolutional neural network, genetic algorithm.

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

“…Based on long-term GPS (global positioning system) data, Li D introduced OD attribute and used utility function to explore the influence of destination attribute on path selection [11]. Ou J proposed a new framework for estimating dynamic OD flow using machine learning algorithm, and carried out an evaluation experiment on the real network of Kunshan City [12]. Duan Z proposed a hybrid neural network prediction model, which can effectively predict the OD traffic of the urban taxi [13].…”

“…Direction and stations of Nanjing metro lines 15,14,13,12,11,10,9,8,7,6,5,41,42,43,44,45,46,47,48,49,50,. 51, 52, 53, 54, 55] …”

A Trial‐and‐Error Congestion Pricing Method for Day‐to‐Day Dynamic Network Flows considering Travelers’ Heterogeneous Inertia Patterns

“…As discussed above, in order to obtain the optimal link tolls, we need to solve problem (14), which requires the demand for each OD pair as its input. However, existing origin-destination survey or data collection technology is not able to evaluate the OD demand precisely [39][40][41][42]. To deal with this issue, Yang et al [11] developed a trial-and-error congestion pricing method to find the system optimal link flows and link tolls.…”

Commuting Pattern Recognition Using a Systematic Cluster Framework

“…Our previous study [25] estimated the O-D patterns using vehicle trajectory data collected by ALPR devices and investigated the temporal-spatial distribution patterns of trip generation and attraction, etc. [26]. With detailed information, large sample size, and real-time data availability of ALPR data [28], these studies highlighted their potentials in individual level traffic pattern recognition.…”

“…Recently, with the emerging big data technologies [13], the commuting pattern at an individual level can be efficiently derived using advanced data-driven methods (e.g., machine learning) [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Various kinds of data were utilized in these data-driven based methods, including Global Positioning System (GPS) data, mobile phone call detail records (CDRs), smart card data and remote sensing imagery [14][15][16], which provide new sights for traffic control-oriented applications.…”