Spatio-Temporal Short-Term Urban Traffic Volume Forecasting Using Genetically Optimized Modular Networks

Abstract:   Current interest in short-term traffic volume forecasting focuses on incorporating temporal and spatial volume characteristics in the forecasting process. This article addresses the problem of integrating and optimizing predictive information from multiple locations of an urban signalized arterial and proposes a modular neural predictor consisting of temporal genetically optimized structures of multilayer perceptrons (MLP) that are fed with volume data from sequential locations to improve the accuracy of short-term forecasts. The results show that the proposed methodology provides more accurate forecasts compared to the conventional statistical methodologies applied, as well as to the static forms of neural networks.     

Dynamic Optimal Traffic Assignment and Signal Time Optimization Using Genetic Algorithms

Abstract:   In this article a dynamic system-optimal traffic assignment model is formulated for a congested urban road network with a number of signalized intersections. A simulation-based approach is employed for the case of multiple-origin-multiple-destination traffic flows. The artificial intelligence technique of genetic algorithms (GAs) is used to minimize the overall travel cost in the network with fixed signal timings and optimization of signal timings. The proposed method is applied to the example network and results are discussed. It is concluded that GAs allow the relaxation of many of the assumptions that may be needed to solve the problem analytically by traditional methods.     

Temporal Evolution of Short-Term Urban Traffic Flow: A Nonlinear Dynamics Approach

Abstract:   Recognizing temporal patterns in traffic flow has been an important consideration in short-term traffic forecasting research. However, little work has been conducted on identifying and associating traffic pattern occurrence with prevailing traffic conditions. We propose a multilayer strategy that first identifies patterns of traffic based on their structure and evolution in time and then clusters the pattern-based evolution of traffic flow with respect to prevailing traffic flow conditions. Temporal pattern identification is based on the statistical treatment of the recurrent behavior of jointly considered volume and occupancy series; clustering is done via a two-level neural network approach. Results on urban signalized arterial 90-second traffic volume and occupancy data indicate that traffic pattern propagation exhibits variability with respect to its statistical characteristics such as deterministic structure and nonlinear evolution. Further, traffic pattern clustering uncovers four distinct classes of traffic pattern evolution, whereas transitional traffic conditions can be straightforwardly identified .     

Data Fusion of Fixed Detector and Probe Vehicle Data for Incident Detection

An important feature of many advanced traveler information systems (ATIS) is real-time information about incidents on the street network. This paper describes a system for automatically detecting incidents for such an ATIS developed using artificial neural networks and statistical prediction methods. The system monitors traffic conditions using two types of data: inductive loop detectors (ILDs) and vehicle probes. For both neural network and statistical methods, incident detection is accomplished using two approaches: by processing traffic input data directly and by processing the output of specialized algorithms that detect incidents using information from each data source. Analysis data generated from a simulation of a typical suburban signalized major arterial street are used. Different model configurations are examined and tested to identify the input variables and methods that are the best predictors of incident occurrence. The neural network approaches consistently perform at least as well as the discriminant analysis models, especially when results are adjusted to avoid false alarms.     

The Optimization of Signal Settings on a Signalized Roundabout Using the Cross-entropy Method

Abstract:   The cross-entropy method (CEM) is a newly developed approach to the solution of complex combinatorial optimization problems. It is an iterative process that consists of generating solutions from some probability distribution whose parameter values are updated in each iteration using information from the best solutions found in that iteration. The article applies the method to the problem of the optimization of signal settings on a signalized roundabout. The performance of any given set of timings is evaluated using the cell transmission model, a deterministic macroscopic traffic flow model that permits the modeling of the spatial extent of queues and the possibility of "blocking back." The results from the investigations are encouraging, and show that the CEM has the potential to be a useful technique for tackling global optimization problems.     

Detection of critical road roughness sections by trend analysis and investigation of driver speed interaction

Pavement roughness (IRI—International Roughness Index values) influence the stability of traffic movements both on intercity roads and urban roads. This study is to determine the exact locations of critical pavement roughness values that affect traffic motion stability and comfort in city centre highway arteries. Roughness data with 10 m intervals were collected on a 3140 m divided road containing three consecutive signalized intersections in the city centre arterial. These data were analysed using the distance-dependent Mann-Kendall trend analysis method and checkerboard model. The sections where roughness is important were determined at a 95% confidence interval. The results will show where future pavement improvements should be prioritized for municipalities and road maintenance engineers and will form a basis for the urban road management system.     

Increasing Signalized Intersection Capacity with Unconventional Use of Special Width Approach Lanes

Heavy traffic volume coupled with insufficient capacity due to limited space cause most of traffic congestion at urban signalized intersections. This article presents an innovative design to increase the capacity of heavily congested intersections by using the special width approach lane (SWAL), which consists of two narrow approach lanes that are dynamically utilized by either two passenger cars or a heavy vehicle (e.g., buses or trucks) depending on the composition of traffic. The impact of the SWAL on the saturation flow rate is quantified and validated, followed by an optimization model for best geometric layout and signal timing design with the presence of the SWAL. The optimization model is formulated as a mixed‐integer‐linear‐program for intersection capacity maximization which can be efficiently solved by the standard branch‐and‐bound technique. Results of extensive numerical analyses and case studies show the effectiveness of SWAL to increase intersection capacity, indicating its promising application at intersections with very limited space that prevents the addition of separate lanes.     

A Bi‐Level Model for Planning Signalized and Uninterrupted Flow Intersections in an Evacuation Network

Abstract: The problem to be addressed in this paper is the lack of an advanced model in the literature to locate the optimal set of intersections in the evacuation network for implementing uninterrupted flow and signal control strategies, respectively, which can yield the maximum evacuation operational efficiency and the best use of available budgets. An optimization model, proposed in response to such needs, contributes to addressing the following critical questions that have long challenged transportation authorities during emergency planning, namely: given the topology of an evacuation network, evacuation demand distribution, and a limited budget, (1) how many intersections should be implemented with the signals and uninterrupted flow strategies; (2) what are their most appropriate locations; and (3) how should turning restriction plans be properly designed for those uninterrupted flow intersections? The proposed model features a bi‐level framework. The upper level determines the best locations for uninterrupted flow and signalized intersections as well as the corresponding turning restriction plans by minimizing the total evacuation time, while the lower level handles routing assignments of evacuation traffic based on the stochastic user equilibrium (SUE) principle. The proposed model is solved by a genetic algorithm (GA) ‐based heuristic. Extensive analyses under various evacuation demand and budget levels have indicated that the location selection of uninterrupted flow and signalized intersections plays a key role in emergency traffic management. The proposed model substantially outperforms existing practices in prioritizing limited resources to the most appropriate control points by significantly reducing the total evacuation time (up to 39%).     

Spatiotemporal gated graph attention network for urban traffic flow prediction based on license plate recognition data

The accurate forecasting of traffic states is an essential application of intelligent transportation system. Due to the periodic signal control at intersections, the traffic flow in an urban road network is often disturbed and expresses intermittent features. This study proposes a forecasting framework named the spatiotemporal gated graph attention network (STGGAT) model to achieve accurate predictions for network-scale traffic flows on urban roads. Based on license plate recognition (LPR) records, the average travel times and volume transition relationships are estimated to construct weighted directed graphs. The proposed STGGAT model integrates a gated recurrent unit layer, a graph attention network layer with edge features, a gated mechanism based on the bidirectional long short-term memory and a residual structure to extract the spatiotemporal dependencies of the approach- and lane-level traffic volumes. Validated on the LPR system in Changsha, China, STGGAT demonstrates superior accuracy and stability to those of the baselines and reveals its inductive learning and fault tolerance capabilities.     

Wavelet Packet-Autocorrelation Function Method for Traffic Flow Pattern Analysis

Abstract:   Accurate and timely forecasting of traffic flow is of paramount importance for effective management of traffic congestion in intelligent transportation systems. A detailed understanding of the properties of traffic flow is essential for building a reliable forecasting model. The discrete wavelet packet transform (DWPT) provides more coefficients than the conventional discrete wavelet transform (DWT), representing additional subtle details of a signal. In wavelet multiresolution analysis, an important decision is the selection of the decomposition level. In this research, the statistical autocorrelation function (ACF) is proposed for the selection of the decomposition level in wavelet multiresolution analysis of traffic flow time series. A hybrid wavelet packet-ACF method is proposed for analysis of traffic flow time series and determining its self-similar, singular, and fractal properties. A DWPT-based approach combined with a wavelet coefficients penalization scheme and soft thresholding is presented for denoising the traffic flow. The proposed methodology provides a powerful tool in removing the noise and identifying singularities in the traffic flow. The methods created in this research are of value in developing accurate traffic-forecasting models .     

Fuzzy Cellular Automata Model for Signalized Intersections

At signalized intersections, the decision‐making process of each individual driver is a very complex process that involves many factors. In this article, a fuzzy cellular automata (FCA) model, which incorporates traditional cellular automata (CA) and fuzzy logic (FL), is developed to simulate the decision‐making process and estimate the effect of driving behavior on traffic performance. Different from existing models and applications, the proposed FCA model utilizes fuzzy interface systems (FISs) and membership functions to simulate the cognition system of individual drivers. Four FISs are defined for each decision‐making process: car‐following, lane‐changing, amber‐running, and right‐turn filtering. A field observation study is conducted to calibrate membership functions of input factors, model parameters, and to validate the proposed FCA model. Simulation experiments of a two‐lane system show that the proposed FCA model is able to replicate decision‐making processes and estimate the effect on overall traffic performance.     

A Linear Model for the Continuous Network Design Problem

Abstract:   This article is concerned with the continuous network design problem on traffic networks, assuming system optimum traffic flow conditions and time-dependent demand. A linear programming formulation is introduced based on a dynamic traffic assignment (DTA) model that propagates traffic according to the cell transmission model. The introduced approach is limited to continuous link improvements and does not provide for new link additions. The main contribution of the article is to provide an analytical formulation for network design that accounts for DTA conditions that can be used for further analysis and extensions. The model is tested on a single destination example network, resembling a freeway corridor, for various congestion levels, loading patterns and budget sizes, to demonstrate the simplicity and effectiveness of the approach.     

An Analytical Approach for Estimating the Highway Capacity Manual Signalized Intersection Delay Variability

Abstract: This article developed a generalized model incorporating three stochastic input variables in the Highway Capacity Manual (HCM) delay equation and analyzed the delay variability explicitly considering variations in key input variables including traffic volume, effective green time, and saturation flow rate. An integration method was used for calculations of mean and variance of the HCM delay. Unlike the previous Expectation Function Method, the proposed integration method can be applied for both undersaturated and oversaturated situations. The applicability of the proposed methodology was demonstrated through a hypothetical case study for a lane group at an isolated signalized intersection. The effects of stochastic variables (e.g., traffic volume, saturation flow rate, and effective green time) and correlations among these variables in the HCM delay were examined.     

A Probabilistic Framework for Bayesian Adaptive Forecasting of Project Progress

Abstract:   A methodology to forecast project progress and final time-to-completion is developed. An adaptive Bayesian updating method is used to assess the unknown model parameters based on recorded data and pertinent prior information. Recorded data can include equality, upper bound, and lower bound data. The proposed approach properly accounts for all the prevailing uncertainties, including model errors arising from an inaccurate model form or missing variables, measurement errors, statistical uncertainty, and volitional uncertainty .
As an illustration of the proposed approach, the project progress and final time-to-completion of an example project are forecasted. For this illustration construction of civilian nuclear power plants in the United States is considered. This application considers two cases (1) no information is available prior to observing the actual progress data of a specified plant and (2) the construction progress of eight other nuclear power plants is available. The example shows that an informative prior is important to make accurate predictions when only a few records are available. This is also the time when forecasts are most valuable to the project manager. Having or not having prior information does not have any practical effect on the forecast when progress on a significant portion of the project has been recorded .     

A hybrid approach for the analysis and prediction of elevator passenger flow in an office building

In an Elevator Group Control System (EGCS), the analysis and prediction of elevator traffic can improve service quality and system performance. For this purpose, we propose a new hybrid approach to analyze and predict elevator passenger flow. In this approach, nonlinear analysis methods are used to reveal the internal dynamic characteristics of passenger flow time series collected from an office building. The results suggest that passenger flow has obvious fractal and chaos characteristics. Based on these characteristics, the support vector machine (SVM) method and fuzzy information granulation (FIG) method are employed to predict passenger flow. The simulation results suggest that the accuracy of passenger flow prediction can meet the identification requirements of elevator traffic patterns in an EGCS. Therefore, the proposed approach can effectively address the passenger flow analysis and prediction problems of office buildings and the results can be used as a foundation for practical application in an EGCS.     

Modeling of Traffic Excitation for System Identification of Bridge Structures

Abstract:   In long-term health monitoring of bridge structures, system identification is often performed based only on the system output (bridge vibration responses) because the system input (traffic excitation) is difficult to measure. To facilitate the identification of the bridge properties, traffic excitation is commonly modeled as spatially uncorrelated white noise. A physical model of a stationary stream of vehicles (moving loads) arriving in accordance with a Poisson process, traversing an elastic beam, shows that the traffic excitation is spatially correlated. Employing the dynamic nodal loading approach, this spatial correlation results in a frequency-dependent excitation spectrum density matrix, and shifts the response spectra obtained from those excited by spatially uncorrelated white noise. It is shown that the application of system identification techniques based on the conventional excitation model may result in misleading structural properties. Hence, this study further proposes an output-only gray-box identification technique for bridge structures, in which knowledge about the nature of the traffic excitation, such as its spatial correlation, is implanted into an autoregressive-moving-average (ARMA) model. The identifiability of the ARMA model so constructed is assured and the feasibility of the proposed identification technique is demonstrated by a numerical example.     

信号控制交叉口进口道公共汽车停靠影响分析

本文在分析信号控制交叉口进口道公共汽车 (以下简称公交) 停靠时间特性的基础上,对国内典型的进口道公交停靠现象进行了分类与总结,并主要针对公交占用机动车外侧混合车道停靠的情况进行了定量分析,得出了公交停靠损失时间、站台位置及单位小时公交停靠车辆数等参数与交叉口交通延误、通行能力等交通效益指标变化情况的相互关系,从而为合理设置公交线路及站台位置,评价公交影响及运行效益提供了理论基础与分析工具。     

Random Process Model for Urban Traffic Flow Using a Wavelet‐Bayesian Hierarchical Technique

Abstract: The existing well‐known short‐term traffic forecasting algorithms require large traffic flow data sets, including information on current traffic scenarios to predict the future traffic conditions. This article proposes a random process traffic volume model that enables estimation and prediction of traffic volume at sites where such large and continuous data sets of traffic condition related information are unavailable. The proposed model is based on a combination of wavelet analysis (WA) and Bayesian hierarchical methodology (BHM). The average daily “trend” of urban traffic flow observations can be reliably modeled using discrete WA. The remaining fluctuating parts of the traffic volume observations are modeled using BHM. This BHM modeling considers that the variance of the urban traffic flow observations from an intersection vary with the time‐of‐the‐day. A case study has been performed at two busy junctions at the city‐centre of Dublin to validate the effectiveness of the strategy.     

An approach for modelling CO2 emissions from road traffic in urban areas

An approach that incorporates three modelling components has been developed to estimate road traffic CO2 emissions for an urban area with street level resolution. The first component enables the determination of the road traffic characteristics using the SATURN (Simulation and Assignment of Traffic in Urban Road Networks) model. The output from this component is then analysed using MATrix LABoratory (MATLAB) programming to provide estimates of CO2 emissions for the urban area. Finally, ArcGIS is used to illustrate the model output. The three components are integrated using a Loose-Coupling approach in which the individual components each load the necessary data to give an independent output. The model structure is discussed in the current paper and the modelling results for a small city (Norwich, UK) are presented.     

许可相位下左转车道存储段长度计算

以左转交通量、周期长度、左转绿灯时间、对向交通量为变量,运用交通流理论和排队论,从满足左转排队车辆停车需求的角度,对信号交叉口进口道设置左转许可相位和一条左转专用车道时的左转车道存储段长度展开研究。基于M/M/1排队系统,建立许可相位下单条左转车道存储段长度计算的理论模型。在此基础上,讨论模型的适用条件和实用性,分析存储段长度随饱和度、左转交通量的变化规律,综合考虑空间几何条件、服务质量、经济性能,指出排队车辆数不宜超过20辆,如置信概率为95%,则实用饱和度区间为[0,0.8]。讨论置信概率、设计交通量及单位车辆停放长度等参数的取值方法。对典型情况计算、分析,提出极限交通量、极限排队长度、临界交通量、临界排队长度两组便于工程应用的概念,并绘制图表。制定应用图表确定存储段长度的流程;讨论设计快速校核表的制定和应用。该研究为交叉口采用左转许可相位且进口道仅设置一条左转车道的存储段长度确定提供依据,也可作为左转专用相位的设置依据。