A shockwave profile model for traffic flow on congested urban arterials

In this paper a new traffic flow model for congested arterial networks, named shockwave profile model (SPM), is presented. Taking advantage of the fact that traffic states within a congested link can be simplified as free-flow, saturated, and jammed conditions, SPM simulates traffic dynamics by analytically deriving the trajectories of four major shockwaves: queuing, discharge, departure, and compression waves. Unlike conventional macroscopic models, in which space is often discretized into small cells for numerical solutions, SPM treats each homogeneous road segment with constant capacity as a section; and the queuing dynamics within each section are described by tracing the shockwave fronts. SPM is particularly suitable for simulating traffic flow on congested signalized arterials especially with queue spillover problems, where the steady-state periodic pattern of queue build-up and dissipation process may break down. Depending on when and where spillover occurs along a signalized arterial, a large number of queuing patterns may be possible. Therefore it becomes difficult to apply the conventional approach directly to track shockwave fronts. To overcome this difficulty, a novel approach is proposed as part of the SPM, in which queue spillover is treated as either extending a red phase or creating new smaller cycles, so that the analytical solutions for tracing the shockwave fronts can be easily applied. Since only the essential features of arterial traffic flow, i.e., queue build-up and dissipation, are considered, SPM significantly reduces the computational load and improves the numerical efficiency. We further validated SPM using real-world traffic signal data collected from a major arterial in the Twin Cities. The results clearly demonstrate the effectiveness and accuracy of the model. We expect that in the future this model can be applied in a number of real-time applications such as arterial performance prediction and signal optimization.     

Modeling capacity flexibility of transportation networks

Flexibility of the transportation system is one of the important performance measures needed to deal with demand changes. In this paper, we provide a quantitative assessment of capacity flexibility for the passenger transportation network using bi-level network capacity models. Two approaches for assessing the value of capacity flexibility are proposed. One approach is based on the concept of reserve capacity, which reflects the flexibility with respect to changes in terms of demand volume only. The second approach allows for variations in the demand pattern in addition to changes in demand volume in order to more fully capture demand changes. Two models are developed in the second approach to consider two types of capacity flexibility. The total capacity flexibility allows all users to have both route choice and destination choice when estimating capacity flexibility. The limited capacity flexibility estimates how much more demand volume could be added to a fixed demand pattern by allowing the additional demand to deviate from the fixed demand pattern. Numerical examples are provided to demonstrate the different concepts of capacity flexibility for a passenger transportation system under demand changes.     

Prediction of travel time variability for cost-benefit analysis

Unreliable travel times cause substantial costs to travelers. Nevertheless, they are often not taken into account in cost-benefit analyses (CBA), or only in very rough ways. This paper aims at providing simple rules to predict variability, based on travel time data from Dutch highways. Two different concepts of travel time variability are used, which differ in their assumptions on information availability to drivers. The first measure is based on the assumption that, for a given road link and given time of day, the expected travel time is constant across all working days (rough information: RI). In the second case, expected travel times are assumed to reflect day-specific factors such as weather conditions or weekdays (fine information: FI). For both definitions of variability, we find that the mean travel time is a good predictor. On average, longer delays are associated with higher variability. However, the derivative of variability with respect to delays is decreasing in delays. It can be shown that this result relates to differences in the relative shares of observed traffic ‘regimes’ (free-flow, congested, hyper-congested) in the mean delay. For most CBAs, no information on the relative shares of the traffic regimes is available. A non-linear model based on mean travel times can then be used as an approximation.     

Surface street traffic volume and single-family house price

This study investigates the relationship between surface street traffic volume and single-family house prices in a relatively small city in the US. Hedonic price models are estimated using data from 9670 transactions that occurred between January 1998 and March 2011. It is discovered that parcels fronting or adjacent to a high-traffic street sell, on average, at an 8.1% discount compared to similar parcels that are not so situated. Restricting the analysis to parcels on or adjacent to a high-traffic street, house price and traffic volume are found to be negatively related; a doubling of volume from any particular traffic count, ceteris paribus, reduces selling price by an average of 2.1%.     

Traffic noise abatement: How different pavements, vehicle speeds and traffic densities affect annoyance levels

In this paper, annoyance ratings from traffic noise recorded on cobblestones, dense asphalt, and open asphalt rubber pavements are assessed with regard to car speeds and traffic densities. It was found that cobblestones pavements are the most annoying; also while open asphalt rubber pavement imposes less annoyance than dense asphalt it is not significantly different. Higher car speeds always lead to greater annoyance, as does higher traffic densities. LAeq and LAmax correlate well with annoyance, but loudness is the best predictor. Roughness and sharpness exhibit inconsistent interactions.     

Short-term Hourly Traffic Forecasts using Hong Kong Annual Traffic Census

The need for acquiring the current-year traffic data is a problem for transport planners since such data may not be available for on-going transport studies. A method is proposed in this paper to predict hourly traffic flows up to and into the near future, using historical data collected from the Hong Kong Annual Traffic Census (ATC). Two parametric and two non-parametric models have been employed and evaluated in this study. The results show that the non-parametric models (Non-Parametric Regression (NPR) and Gaussian Maximum Likelihood (GML)) were more promising for predicting hourly traffic flows at the selected ATC station. Further analysis encompassing 87 ATC stations revealed that the NPR is likely to react to unexpected changes more effectively than the GML method, while the GML model performs better under steady traffic flows. Taking into consideration the dynamic nature of the common traffic patterns in Hong Kong and the advantages/disadvantages of the various models, the NPR model is recommended for predicting the hourly traffic flows in that region.     

Total cost analysis: An alternative to benefit-cost analysis in evaluating transportation alternatives

This paper explains the theory in support of total cost analysis (TCA) to compare transportation system alternatives. The full costs of each alternative are first aggregated, including travel time costs and monetizable environmental and social costs. Many costs which are considered on the benefits side of the equation in benefit-cost analysis (BCA) as "cost savings" are brought over to the costs side. Total cost differences among alternatives are then traded off against their estimated non-monetized benefits or impacts, just as a consumer trades off product quality against cost before deciding which product he or she will buy. One advantage of TCA over traditional BCA is that the concept of "total cost" is more easily understood by the public and by political decision-makers than BCA concepts such as "net present worth", "benefit-cost ratio" and "internal rate of return". A second advantage is that there is no suggestion that all "benefits" have been considered; decision-makers are free to use their own value judgements to trade off total cost against non-monetizable social, environmental and economic impacts, just as they trade off quality and convenience against cost when purchasing goods and services in their roles as consumers. The TCA approach is demonstrated in this paper through a case study of two systemwide alternatives for the Baltimore, MD urban area.     

Real time queue length estimation for signalized intersections using travel times from mobile sensors

We study how to estimate real time queue lengths at signalized intersections using intersection travel times collected from mobile traffic sensors. The estimation is based on the observation that critical pattern changes of intersection travel times or delays, such as the discontinuities (i.e., sudden and dramatic increases in travel times) and non-smoothness (i.e., changes of slopes of travel times), indicate signal timing or queue length changes. By detecting these critical points in intersection travel times or delays, the real time queue length can be re-constructed. We first introduce the concept of Queue Rear No-delay Arrival Time which is related to the non-smoothness of queuing delay patterns and queue length changes. We then show how measured intersection travel times from mobile sensors can be processed to generate sample vehicle queuing delays. Under the uniform arrival assumption, the queuing delays reduce linearly within a cycle. The delay pattern can be estimated by a linear fitting method using sample queuing delays. Queue Rear No-delay Arrival Time can then be obtained from the delay pattern, and be used to estimate the maximum and minimum queue lengths of a cycle, based on which the real-time queue length curve can also be constructed. The model and algorithm are tested in a field experiment and in simulation.     

Real-time freeway traffic state estimation based on extended Kalman filter: Adaptive capabilities and real data testing

This paper reports on real data testing of a real-time freeway traffic state estimator, with a particular focus on its adaptive capabilities. The pursued general approach to the real-time adaptive estimation of complete traffic state in freeway stretches or networks is based on stochastic macroscopic traffic flow modeling and extended Kalman filtering. One major innovative feature of the traffic state estimator is the online joint estimation of important model parameters (free speed, critical density, and capacity) and traffic flow variables (flows, mean speeds, and densities), which leads to three significant advantages of the estimator: (1) avoidance of prior model calibration; (2) automatic adaptation to changing external conditions (e.g. weather and lighting conditions, traffic composition, control measures); (3) enabling of incident alarms. These three advantages are demonstrated via suitable real data testing. The achieved testing results are satisfactory and promising for subsequent applications.     

一级公路交叉口设计中的交通安全问题及对策

一级干线公路由于开口较多,不可避免地存在较多的交通安全问题.文章以湖北荆监一级公路为例,针对交叉口设计中的交通安全典型问题,提出了改善方法及相关对策.