Parameter estimation for the peak traffic model

In a previous article a model was developed for predicting the temporal distribution of peak traffic demand, and the model is sensitive to the determining cost parameters whose values were not known. These costs are the cost to late and early arrivals at work and the cost to delays in the system while travelling. In this paper, using the method of least squares, representative values for these cost parameters are estimated for both the Southbound and Northbound traffic using the Sydney Harbour Bridge during the morning peak period. The resulting estimates show that a traveller tends to attach much higher cost to delays than to earliness or lateness to work; although the relative cost he attaches to lateness is higher than he attaches to earliness.     

Experienced travel time prediction for congested freeways

Travel time is an important performance measure for transportation systems, and dissemination of travel time information can help travelers make reliable travel decisions such as route choice or departure time. Since the traffic data collected in real time reflects the past or current conditions on the roadway, a predictive travel time methodology should be used to obtain the information to be disseminated. However, an important part of the literature either uses instantaneous travel time assumption, and sums the travel time of roadway segments at the starting time of the trip, or uses statistical forecasting algorithms to predict the future travel time. This study benefits from the available traffic flow fundamentals (e.g. shockwave analysis and bottleneck identification), and makes use of both historical and real time traffic information to provide travel time prediction. The methodological framework of this approach sequentially includes a bottleneck identification algorithm, clustering of traffic data in traffic regimes with similar characteristics, development of stochastic congestion maps for clustered data and an online congestion search algorithm, which combines historical data analysis and real-time data to predict experienced travel times at the starting time of the trip. The experimental results based on the loop detector data on Californian freeways indicate that the proposed method provides promising travel time predictions under varying traffic conditions.     

A robust approach for road users classification using the motion cues

Video monitoring of traffic is a common practice in major cities. The data generated by video monitoring has practical uses such as traffic analysis for city planning. However, the usefulness of video monitoring of traffic is limited unless there is also a reliable way to automatically classify road users. This paper presents an automated method of road users’ classification into vehicles, cyclists, and pedestrians by using their motion cues. In this method, the movement of road users was captured on sequences of video frames. The videos were analysed using a feature-based tracking system, which has returned the tracks of road users. The separate pieces of information gained from these tracks are hereafter called Classifiers. There are nineteen classifiers included in this method. The classifiers’ values were assessed and integrated into a fuzzy membership framework, which in turn required prior configurations to be available. This led to the final classification of road users. The performance of this method demonstrated promising results. An important contribution of this paper is the creation of a robust approach that can integrate different classifiers using fuzzy membership framework. The developed method also uses parametric classifiers, which do not depend on the specific geometry or traffic operation of the intersection. This is a key advantage because it enables transferability and improves the practicality and usefulness of the method.     

Development of a sensor system for traffic data collection

Although many types of traffic sensors are currently in use, all have some drawbacks, and widespread deployment of such sensor systems has been difficult due to high costs. Due to these deficiencies, there is a need to design and evaluate a low cost sensor system that measures both vehicle speed and counts. Fulfilling this need is the primary objective of this research. Compared to the many existing infrared-based concepts that have been developed for traffic data collection, the proposed method uses a transmission-based type of optical sensor rather than a reflection-based type. Vehicles passing between sensors block transmission of the infrared signal, thus indicating the presence of a vehicle. Vehicle speeds are then determined using the known distance between multiple pairs of sensors. A prototype of the sensor system, which uses laser diode and photo detector pairs with the laser directly projected onto the photo detector, was first developed and tested in the laboratory. Subsequently this experimental prototype was implemented for field testing. The traffic flow data collected were compared to manually collected vehicle speed and traffic counts and a statistical analysis was done to evaluate the accuracy of the sensor system. The analysis found no significant difference between the data generated by the sensor system and the data collected manually at a 95% confidence interval. However, the testing scenarios were limited and so further analysis is necessary to determine the applicability in more congested urban areas. The proposed sensor system, with its simple technology and low cost, will be suitable for saturated deployment to form a densely distributed sensor network and can provide unique support for efficient traffic incident management. Additionally, because it may be quickly installed in the field without the need of elaborate fixtures, it may be deployed for use in temporary traffic management applications such as traffic management in road work zones or during special events.     

A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters

With trajectory data, a complete microscopic and macroscopic picture of traffic flow operations can be obtained. However, trajectory data are difficult to observe over large spatiotemporal regions—particularly in urban contexts—due to practical, technical and financial constraints. The next best thing is to estimate plausible trajectories from whatever data are available. This paper presents a generic data assimilation framework to reconstruct such plausible trajectories on signalized urban arterials using microscopic traffic flow models and data from loops (individual vehicle passages and thus vehicle counts); traffic control data; and (sparse) travel time measurements from whatever source available. The key problem we address is that loops suffer from miss- and over-counts, which result in unbounded errors in vehicle accumulations, rendering trajectory reconstruction highly problematic. Our framework solves this problem in two ways. First, we correct the systematic error in vehicle accumulation by fusing the counts with sparsely available travel times. Second, the proposed framework uses particle filtering and an innovative hierarchical resampling scheme, which effectively integrates over the remaining error distribution, resulting in plausible trajectories. The proposed data assimilation framework is tested and validated using simulated data. Experiments and an extensive sensitivity analysis show that the proposed method is robust to errors both in the model and in the measurements, and provides good estimations for vehicle accumulation and vehicle trajectories with moderate sensor quality. The framework does not impose restrictions on the type of microscopic models used and can be naturally extended to include and estimate additional trajectory attributes such as destination and path, given data are available for assimilation.     

Development of crash prediction models with individual vehicular data

Typical engineering research on traffic safety focuses on identifying either dangerous locations or contributing factors through a post-crash analysis using aggregated traffic flow data and crash records. A recent development of transportation engineering technologies provides ample opportunities to enhance freeway traffic safety using individual vehicular information. However, little research has been conducted regarding methodologies to utilize and link such technologies to traffic safety analysis. Moreover, traffic safety research has not benefited from the use of hurdle-type models that might treat excessive zeros more properly than zero-inflated models.This study developed a new surrogate measure, unsafe following condition (UFC), to estimate traffic crash likelihood by using individual vehicular information and applied it to basic sections of interstate highways in Virginia. Individual vehicular data and crash data were used in the development of statistical crash prediction models including hurdle models. The results showed that an aggregated UFC measure was effective in predicting traffic crash occurrence, and the hurdle Poisson model outperformed other count data models in a certain case.     

Short-term traffic flow rate forecasting based on identifying similar traffic patterns

The ability to timely and accurately forecast the evolution of traffic is very important in traffic management and control applications. This paper proposes a non-parametric and data-driven methodology for short-term traffic forecasting based on identifying similar traffic patterns using an enhanced K-nearest neighbor (K-NN) algorithm. Weighted Euclidean distance, which gives more weight to recent measurements, is used as a similarity measure for K-NN. Moreover, winsorization of the neighbors is implemented to dampen the effects of dominant candidates, and rank exponent is used to aggregate the candidate values. Robustness of the proposed method is demonstrated by implementing it on large datasets collected from different regions and by comparing it with advanced time series models, such as SARIMA and adaptive Kalman Filter models proposed by others. It is demonstrated that the proposed method reduces the mean absolute percent error by more than 25%. In addition, the effectiveness of the proposed enhanced K-NN algorithm is evaluated for multiple forecast steps and also its performance is tested under data with missing values. This research provides strong evidence suggesting that the proposed non-parametric and data-driven approach for short-term traffic forecasting provides promising results. Given the simplicity, accuracy, and robustness of the proposed approach, it can be easily incorporated with real-time traffic control for proactive freeway traffic management.     

A new framework for the integration,analysis and visualisation of urban traffic data within geographic information systems

Current geographical information systems (GIS) are not well adapted to the management of very dynamic geographical phenomena. This is due to the lack of conceptual and physical interoperability with real-time computing facilities. The research described in this paper is oriented towards the identification and experimentation of a new methodological and applied framework for the real-time integration, manipulation and visualisation of urban traffic data. It is based on proactive interaction between the spatio-temporal database and visualisation levels, and between the visualisation and end-user levels. The proposed framework integrates different spatial and temporal levels of granularity during the analysis of urban traffic data. Urban traffic behaviours are analysed either by observation of the movements of several vehicles in space, or by changes in urban network properties (i.e., micro- versus macro-modelling). Visualisation and interaction tools together constitute a flexible interface environment for the visualisation of urban traffic data within GIS. These concepts provide a relevant support for the visual analysis of urban traffic patterns in the thematic, spatial and temporal dimensions. This integrated framework is illustrated by an experimental prototype developed in a large town in the UK.     

Direct versus indirect models for the effects of unreliability

Two distinguishable modelling approaches exist for modelling the attitudes of travellers to the unexpected day-to-day variability of travel times. The direct approach sees the extent of travel time variability (TTV) as the variable that travellers react to, whereas the indirect approach claims that TTV effects are fully explained by trip scheduling considerations. Past research has not yet overcome the issue of which of these concepts is preferable, especially for public transport users. In the current paper, factors affecting bus users’ scheduling behaviour and attitudes to TTV are investigated, based on a survey among bus users in the city of York, England. The survey methodology and its Internet-based design are described. The results confirm that the influence of TTV on bus users is best explained indirectly through scheduling considerations. The penalty placed on early arrival to the destination is found similar to the penalty on travel time itself; late arrivals are much more heavily penalised. Since the common treatment of TTV in practice is through models that ignore the effect of lateness and earliness, we also examine how using the simple approach rather than the correct one affects the economic interpretation of TTV; the results reveal a massive bias.     

Estimation of missing traffic counts using factor,genetic, neural,and regression techniques

Analyses from some of the highway agencies show that up to 50% permanent traffic counts (PTCs) have missing values. It will be difficult to eliminate such a significant portion of data from traffic analysis. Literature review indicates that the limited research uses factor or autoregressive integrated moving average (ARIMA) models for predicting missing values. Factor-based models tend to be less accurate. ARIMA models only use the historical data. In this study, genetically designed neural network and regression models, factor models, and ARIMA models were developed. It was found that genetically designed regression models based on data from before and after the failure had the most accurate results. Average errors for refined models were lower than 1% and the 95th percentile errors were below 2% for counts with stable patterns. Even for counts with relatively unstable patterns, average errors were lower than 3% in most cases.     

Modeling traffic's flow-density relation: Accommodation of multiple flow regimes and traveler types

This research investigates freeway-flow impacts of different traveler types by specifying and applying a latent-segmentation model of congested and uncongested driving behaviors. Drivers in uncongested conditions are assumed to drive at self-chosen speeds, while drivers in congested conditions are assumed to take speed as given and choose a spacing (between their vehicle and the previous vehicle). Several classes of driver-vehicle combinations are distinguished in a data set based on double-loop-detector pulses and a household travel survey. These classifications are made on the basis of vehicle type and gender, leading to class estimates of speeds and spacings. The segmentation model is specified as a logit function of density, weather, and vehicle type, leading to estimates of congested-condition probabilities. Unobserved heterogeneity is incorporated in all models via common error assumptions.Results indicate that segmentation models are promising tools for traffic data analysis and that information on travelers, their vehicles, and weather conditions explains significant variation in flow data. By clarifying a greater understanding of traffic conditions and traveler behavior explains much scatter in the fundamental relation between flow, speed, and density, can assist regions in their traffic-management efforts and engineers in their design of roadway facilities. Ultimately, such improvements to travel networks should enhance quality of life.     

Assessing greenhouse gas and related air pollutant emissions from road traffic counts: A case study for Mauritius

The road transport sector is one of the major contributors of greenhouse gases and other air pollutants emissions. Regional emissions levels from road vehicles were investigated, in Mauritius, by applying a fuel-based approach. We estimated fuel consumption and air emissions based on traffic counts on the various types of classified roads at three different regional set ups, namely urban, semi urban and rural. The Relative Development Index (RDI), a composite index calculated from socio-economic and environmental indicators was used to classify regions. Our results show that the urban motorways were the most polluting due to heavy traffic. Some rural areas had important pollution levels as well. Our analysis of variance (ANOVA), however, showed little difference in emissions among road types and regions. The study can provide a simple tool for researchers in countries where data are very scarce, as is the case for many developing countries.     

A two-stage fuzzy logic controller for traffic signals

This paper presents the design and evaluation of a fuzzy logic traffic signal controller for an isolated intersection. The controller is designed to be responsive to real-time traffic demands. The fuzzy controller uses vehicle loop detectors, placed upstream of the intersection on each approach, to measure approach flows and estimate queues. These data are used to decide, at regular time intervals, whether to extend or terminate the current signal phase. These decisions are made using a two-stage fuzzy logic procedure. In the first stage, observed approach traffic flows are used to estimate relative traffic intensities in the competing approaches. These traffic intensities are then used in the second stage to determine whether the current signal phase should be extended or terminated. The performance of this controller is compared to that of a traffic-actuated controller for different traffic conditions on a simulated four-approach intersection.     

Collecting ambient vehicle trajectories from an instrumented probe vehicle: High quality data for microscopic traffic flow studies

This paper presents the methodology and results from a study to extract empirical microscopic vehicular interactions from a probe vehicle instrumented with sensors to monitor the ambient vehicles as it traverses a 28 mi long freeway corridor. The contributions of this paper are two fold: first, the general method and approach to seek a cost-effective balance between automation and manual data reduction that transcends the specific application. Second, the resulting empirical data set is intended to help advance traffic flow theory in general and car following models in particular. Generally the collection of empirical microscopic vehicle interaction data is either too computationally intensive or labor intensive. Historically automatic data extraction does not provide the precision necessary to advance traffic flow theory, while the labor demands of manual data extraction have limited past efforts to small scales. Key to the present study is striking the right balance between automatic and manual processing. Recognizing that any empirical microscopic data for traffic flow theory has to be manually validated anyway, the present study uses a “pretty good” automated processing algorithm followed by detailed manual cleanup using an efficient user interface to rapidly process the data. The study spans roughly two hours of data collected on a freeway during the afternoon peak of a typical weekday that includes recurring congestion. The corresponding data are being made available to the research community to help advance traffic flow theory in general and car following models in particular.     

Towards a real-time microscopic emissions model

This article presents a new approach to microscopic road traffic exhaust emission modelling. The model described uses data from the SCOOT demand-responsive traffic control system implemented in over 170 cities across the world. Estimates of vehicle speed and classification are made using data from inductive detector loops located on every SCOOT link. This data feeds into a microscopic traffic model to enable enhanced modelling of the driving modes of vehicles (acceleration, deceleration, idling and cruising). Estimates of carbon monoxide emissions are made by applying emission factors from an extensive literature review. A critical appraisal of the development and validation of the model is given before the model is applied to a study of the impact of high emitting vehicles. The article concludes with a discussion of the requirements for the future development and benefits of the application of such a model.     

Study on road section environmental traffic capacity model and algorithm under double constraints

The coordinated development of city traffic and environment is a key research content in traffic field in twenty-first Century. Among them, road section environmental traffic capacity analysis is one of the important research issues. It can provide solid theoretical basis and reliable data support for road network traffic optimization control, road traffic pollution control and city traffic structure optimization. This paper analyzed main factors which impacted environmental traffic capacity from two aspects, including road capacity constraint conditions and road traffic pollution control constraint conditions. Then, road section environmental traffic capacity optimization model was established, and method of improved augmented Lagrange function was used to solve the model. Case study showed that, (1) The environmental traffic capacity optimal model and methodology were effective; (2) In order to ensure road section environmental traffic capacity greater than (or equal to) road capacity, some measures could be taken including adjusting motor vehicle type proportion as well as improving emission characteristics of motor vehicles exhausting pollutants.     

The socio-economic impacts assessment of advanced convoy driving on motorway

This paper presents a recent research work on socio-economic impacts of convoy driving on motorways. Two different methods have been employed for the assessment. The cost–benefit analysis method has been found to be very efficient where costs and benefits can be valued monetarily, while data envelopment analysis can deal with discretionary or intangible impacts that cannot reasonably be expressed in monetary units.The results from both assessment methods have shown that motorway convoy driving may have significant socio-economic benefits when the convoy lane is properly fed with convoy traffic.     

A dynamic approach to road freight flows modeling in Spain

This paper presents an innovative approach to analyzing road vehicle freight traffic that uses a dynamic panel data specification derived from a gravity model. This dynamic approach, which has recently been employed in international goods trade models in lieu of the traditional static specification, is applied to the case of Spain using data for the countrys 15 NUTS-3 regions between 1999 and 2009. Using the system general method of moments approach, we obtained significant evidence that the flow of vehicles carrying commodities by road has a strong persistence effect when controlling for unobserved heterogeneity. We also found that the quality of road transport infrastructure has a significant impact on vehicle trips. According to our findings, we suggest that this type of specification be employed in distribution models in which fixed effects and lags of the dependent variable are included to account for unobserved heterogeneity and persistence effects, respectively.     

Robust calibration of macroscopic traffic simulation models using stochastic collocation

The predictions of a well-calibrated traffic simulation model are much more valid if made for various conditions. Variation in traffic can arise due to many factors such as time of day, work zones and weather. Calibration of traffic simulation models for traffic conditions requires larger datasets to capture the stochasticity in traffic conditions. In this study we use datasets spanning large time periods to incorporate variability in traffic flow, speed for various time periods. However, large data poses a challenge in terms of computational effort. With the increase in number of stochastic factors, the numerical methods suffer from the curse of dimensionality. In this study, we propose a novel methodology to address the computational complexity due to the need for the calibration of simulation models under highly stochastic traffic conditions. This methodology is based on sparse grid stochastic collocation, which, treats each stochastic factor as a different dimension and uses a limited number of points where simulation and calibration are performed. A computationally efficient interpolant is constructed to generate the full distribution of the simulated flow output. We use real-world examples to calibrate for different times of day and conditions and show that this methodology is much more efficient that the traditional Monte Carlo-type sampling. We validate the model using a hold out dataset and also show the drawback of using limited data for the calibration of a macroscopic simulation model. We also discuss the drawbacks of the predictive ability of a single calibrated model for all the conditions.     

基于WOS数据库的文献计量分析的城市交通事故态势研究

为从宏观上了解交通事故的研究态势,利用文献计量法对WOS数据库收录的474篇文献进行数据可视化分析。研究发现,发文量历经了零阶段、稳定阶段和上升阶段;中国研究机构数量和发文量都位于世界第一;研究领域形成了由122位作者组成的核心作者群体;研究方向经历了以交通参与者、道路交通事故、交通事故安全为研究目的的变化;关键词分析得出该领域未来的研究热点将集中在交通事故安全、交通事故严重程度及交通事故影响三方面。