Traffic state estimation on highway: A comprehensive survey

Abstract

Traffic state estimation (TSE) refers to the process of the inference of traffic state variables (i.e., flow, density, speed and other equivalent variables) on road segments using partially observed traffic data. It is a key component of traffic control and operations, because traffic variables are measured not everywhere due to technological and financial limitations, and their measurement is noisy. Therefore, numerous studies have proposed TSE methods relying on various approaches, traffic flow models, and input data. In this review article, we conduct a survey of highway TSE methods, a topic which has gained great attention in the recent decades. We characterize existing TSE methods based on three fundamental elements: estimation approach, traffic flow model, and input data. Estimation approach encompasses methods that estimate the traffic state, based on partial observation and a priori knowledge (assumptions) on traffic dynamics. Estimation approaches can be roughly classified into three according to their dependency on a priori knowledge and empirical data: model-driven, data-driven, and streaming-data-driven. A traffic flow model usually means a physics-based mathematical model representing traffic dynamics, with various solution methods. Input data can be characterized by using three different properties: collection method (stationary or mobile), data representation (disaggregated or aggregated), and temporal condition (real-time or historical). Based on our proposed characterization, we present the current state of TSE research and proposed future research directions. Some of the findings of this article are summarized as follows. We present model-driven approaches commonly used. We summarize the recent usage of detailed disaggregated mobile data for the purpose of TSE. The use of these models and data will raise a challenging problem due to the fact that conventional macroscopic models are not always consistent with detailed disaggregated data. Therefore, we show two possibilities in order to solve this problem: improvement of theoretical models, and the use of data-driven or streaming-data-driven approaches, which recent studies have begun to consider. Another open problem is explicit consideration of traffic demand and route-choice in a large-scale network; for this problem, emerging data sources and machine learning would be useful.

The State-of-the-Art of Coordinated Ramp Control with Mixed Traffic Conditions

“…This data partially reflect the real-time traffic conditions. The goal of traffic state estimation is to provide accurate prediction of the current traffic information both microscopically and macroscopically based on apriori knowledge and partial observation [3]. The Section III provides a comprehensive review of current research of traffic state estimation.…”

“…Since it is difficult and expensive to obtain complete information on the traffic (e.g., 100% penetration rate of connected vehicles), estimation of traffic states, such as flow, density, and speed, from partially observed traffic data plays an important role. Seo et al performed a comprehensive survey about traffic state estimation which provides a guideline into this field [3]. The categories listed below are on the basis of their suggestion.…”

Traffic state estimation using stochastic Lagrangian dynamics

“…For example, stochastic extensions of the cell transmission model [5,6] have been proposed [37,18]; other approaches have extended the link transmission model [44], both at the individual link level and the network level [33,32,34,26]. In general, there still remain issues related to the physical accuracy of the sample paths of existing stochastic traffic models, particularly those developed for purposes of traffic state estimation (see [36,39] for recent reviews). The main culprit is the dominance of time-stochasticity (or noise) in the stochastic models, mostly developed in Eulerian coordinates [15,38,28,16,40,2,41,43,10,37,1,19], but also in Lagrangian coordinates [46,45,3].…”

Multi-hop broadcast message dissemination in vehicular ad hoc networks: A security perspective review

“…Other approaches for VANET-based vehicle density estimation can be reviewed in the survey presented in Darwish and Bakar. 76 Additionally, a comprehensive survey for density estimation techniques based on scenario modeling and historical data is presented in Seo et al 77 If all the vehicles detecting an attack override the rebroadcast decision rule, solutions based on vehicle density estimation could lead to a scenario where all receiving vehicles retransmit the packet. Considering a single attacker, this will occur only in the current hop.…”

“…The periodic exchange of beacon messages in senderoriented broadcast dissemination protocols can be exploited to implement data-centric security solutions. 66,77,85 For instance, in Jaballah et al, 47 a cooperative position verification mechanism, based on the exchange of an active neighbors table, is proposed to secure the broadcast dissemination process.…”