Considerable efforts have been devoted to the development of dynamic origin-destination (OD) estimation models, which are a key step to realizing self-adaptive traffic control systems for urban traffic management. However, most of the models proposed to date estimate OD flows based on a single traffic data source, and their performance is limited by the coverage and accuracy of traffic sensors. The inherent difficulty in estimating the dynamic traffic assignment matrix means that dynamic OD estimation remains a challenge for real-life applications. This paper proposes the use of a Kalman filter for dynamic OD estimation using multi-source sensor data. The dynamic characteristic of changing OD flow over time is analyzed, and the problem of dynamic OD estimation is converted to a problem of estimating OD structural deviation. The resulting dynamic relationship between traffic volume and OD structural deviation is then used to establish the Kalman filter model. An improved traffic assignment approach is developed and embedded into the measurement equation of the Kalman filter model to enable dynamic updating of the traffic assignment matrix. A dual self-adaptive mechanism based on the Kalman filter is used to calibrate the model. The proposed method was implemented on a real-life traffic network in the downtown area of Kunshan City, China. The results show that the proposed method is more accurate than, and outperforms, the traditional link-volume-based and turning-movement-based methods.
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.
Identifying and detecting the travel mode and pattern of individual travelers is an important problem in transportation planning and policy making. Mobile-phone Signaling Data (MSD) have numerous advantages, including wide coverage and low acquisition cost, data stability and reliability, and strong real-time performance. However, due to their noisy and temporally irregular nature, extracting mobility information such as transport modes from these data is particularly challenging. This paper establishes a travel mode identification model based on the MSD combined with residents' travel survey data, Geographic Information System (GIS) data, and navigation data. Using the data obtained from Kunshan, China in 2017, enriched with variables on the travel mode identification, the model achieved a high accuracy of 90%. The accuracy is satisfactory for all of the transport modes other than buses. Furthermore, among the explanatory variables such as the built environment factors (e.g., the coverage rate of a bus stop) are in general more significant, in contrast with other attributes. This indicates that the land use functions are more influential on the travel mode selection as well as the level of travel demand.
Short-term traffic flow forecasting is crucial for proactive traffic management and control. One key issue associated with the task is how to properly define and capture the temporal patterns of traffic flow. A feasible solution is to design a multi-regime strategy. In this paper, an effective approach to forecasting short-term traffic flow based on multi-regime modeling and ensemble learning is presented. First, to properly capture the different patterns of traffic flow dynamics, a regime identification model based on probabilistic modeling was developed. Each identified regime represents a specific traffic phase, and was used as the representative feature for the forecasting modeling. Second, a forecasting model built on an ensemble learning strategy was developed, which integrates the forecasts of multiple regression trees. The traffic flow data over 5-min intervals collected from four I-80 freeway segments, in California, USA, was used to evaluate the proposed approach. The experimental results show that the identified regimes are able to well explain the different traffic phases, and play an important role in forecasting. Furthermore, the developed forecasting model outperformed four typical models in terms of root mean square error (RMSE) and mean absolute percentage error (MAPE) on three traffic flow measures.
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