A time-dependent freight tour synthesis model

This paper introduces a model of urban freight demand that seeks to estimate tour flows from secondary data sources e.g., traffic counts, to bypass the need for expensive surveys. The model discussed in this paper, referred as Freight Tour Synthesis (FTS), enhances current techniques by incorporating the time-dependent tour-based behavior of freight vehicles, and the decision maker’s (e.g., metropolitan planning agency planner) preferences for different sources of information. The model, based on entropy maximization theory, estimates the most likely set of tour flows, given a set of freight trip generation estimates, a set of traffic counts per time interval, and total freight transportation cost in the network. The type of inputs used allows the assessment of changes in infrastructure, policy and land use. The ability of the model to replicate actual values is assessed using the Denver Region (CO) as a case study.     

Empirical analysis of short period traffic counts and their efficiency: the case of Indian traffic

Short period traffic counts (SPTCs) are conducted routinely to estimate the annual average daily traffic (AADT) at a particular site. This paper uses Indian traffic volume data to methodically and extensively study the effect of four aspects related to the design of SPTCs. These four aspects are: (i) for how long, (ii) on which days should SPTCs be carried out, (iii) how many times, and (iv) on which months should SPTCs be carried out? The analyses indicate that the best durations for conducting SPTCs are 3 days (starting with a Thursday) and 7 days, for total traffic and truck traffic, respectively. Further, these counts should be repeated twice a year keeping a separation of two months between the counts to obtain good estimates of AADT at minimal cost. An additional outcome of this study has been the determination of seasonal factor values for roads in developing economies, like India.     

Estimating vehicle miles traveled (VMT) in urban areas using regression kriging

The recent increase in demand for performance‐driven and outcome‐based transportation planning makes accurate and reliable performance measures essential. Vehicle miles traveled (VMT), the total miles traveled by all vehicles on roadways, has been utilized widely as a proxy for traffic impact assessment, vehicle emissions, gasoline consumption, and crashes. Accordingly, a number of studies estimate VMT using diverse data sources. This study estimates VMT in the urban area of Bucheon, South Korea, by predicting the annual average daily traffic for unmeasured locations using spatial interpolation techniques (i.e., regression kriging and linear regression). The predictive performance of this method is compared with that of the existing Highway Performance Monitoring System (HPMS) method. The results show that regression kriging could provide more accurate VMT estimates than the HPMS method and linear regression, especially with a small sample size. Copyright © 2016 John Wiley & Sons, Ltd.     

Development and implementation of the areawide Dynamic ROad traffic NoisE (DRONE) simulator

This paper discusses the areawide Dynamic ROad traffic NoisE (DRONE) simulator, and its implementation as a tool for noise abatement policy evaluation. DRONE involves integrating a road traffic noise estimation model with a traffic simulator to estimate road traffic noise in urban networks. An integrated traffic simulation-noise estimation model provides an interface for direct input of traffic flow properties from simulation model to noise estimation model that in turn estimates the noise on a spatial and temporal scale. The output from DRONE is linked with a geographical information system for visual representation of noise levels in the form of noise contour maps.     

Traffic state estimation and uncertainty quantification based on heterogeneous data sources: A three detector approach

This study focuses on how to use multiple data sources, including loop detector counts, AVI Bluetooth travel time readings and GPS location samples, to estimate macroscopic traffic states on a homogeneous freeway segment. With a generalized least square estimation framework, this research constructs a number of linear equations that map the traffic measurements as functions of cumulative vehicle counts on both ends of a traffic segment. We extend Newell’s method to solve a stochastic three-detector problem, where the mean and variance estimates of cell-based density and flow can be analytically derived through a multinomial probit model and an innovative use of Clark’s approximation method. An information measure is further introduced to quantify the value of heterogeneous traffic measurements for improving traffic state estimation on a freeway segment.     

Lane-harmonised passenger car equivalents for heterogeneous expressway traffic

In order to account for variations in traffic composition during traffic analysis, passenger car equivalent (PCE) factors are used to convert flow rates of various vehicle classes into flow rates in terms of passenger car units (PCUs). Earlier studies have developed various methods to estimate PCE values but only a few of them are based on uninterrupted traffic flow, particularly for flow regimes with heterogeneous traffic where differential (lower) speed limits are imposed on commercial vehicles. This paper proposes a lane-harmonisation approach, which leverages on the high variation in traffic composition across the lanes, to estimate PCE factors for urban expressways. Multiple linear regression is used and the PCE factors obtained for motorcycles, light goods vehicles, and heavy goods vehicles are 0.65, 1.53, and 2.75, respectively. The estimated capacity flow rate after the application of the obtained PCE factors is around 2200 PCUs per hour per lane.     

Real-time estimation of turning movement proportions from partial counts on urban networks

Modern traffic signal control systems require reliable estimates of turning flows in real time to formulate effective control actions, and accommodate disturbances in traffic demand without deteriorating the system performance. The more accurate the estimation is, the more effective the control plan is. Most of the previous research works assumed that a full set of detector counts is available and employed the least-squares methods to produce unbiased estimates of the turning movement proportions. However, in practice, such a dense detector configuration is expensive to install and maintain. Also, the least-squares estimates are not feasible when the travel time between inflows and outflows is significant, or when intervening traffic conditions change the travel time. This study proposes a nonlinear least-square (NLS) approach and a quasi maximum likelihood (QML) approach to recursively estimate turning movement proportions in a network of intersections where only a partial set of detector counts are available. Using large population approximation technique, a class of nonlinear, discrete-time traffic flow models are transformed into a linear state–space model tractable for on-line applications. The quality of estimates is demonstrated by implementing the proposed algorithms with simulation and real data. As a comparison, the NLS estimator shows less bias but with higher variance than the QML estimator. The QML estimator outperforms the NLS estimator in terms of total mean square error, due to an increase in bias being traded for a decrease in variance.     

Simulation‐based estimates of delays at freeway work zones

Work zone related traffic delay is an important cost component on freeways with maintenance activities. This study demonstrates that delays may be underestimated by using the deterministic queuing theory. Computer simulation is a valuable approach of estimating delay under a variety of existing and future conditions. However, a single simulation run, which can be quite costly in terms of both computer and analyst time, produces a delay estimate for only one traffic level under one set of conditions. A method is developed in this paper to approximate delays by integrating limited simulation data, obtained from CORSIM and the concept of deterministic queuing theory, while various geometric conditions and time‐varying traffic distribution are considered. A calibrated and validated simulation model that can reflect work zone traffic operations on a segment of Interstate 1–80 in New Jersey is used to generate data for developing the proposed model. The comparison of delays estimated by the deterministic queuing model and the proposed model is conducted, while factors affecting the accuracy of the delay estimates are discussed.     

Optimal tolls for multi-class traffic: Analytical formulations and policy implications

This paper puts together an analytical formulation to compute optimal tolls for multi-class traffic. The formulation is comprised of two major modules. The first one is an optimization component aimed at computing optimal tolls assuming a Stackelberg game in which the toll agency sets the tolls, and the equilibrating traffic plays the role of the followers. The optimization component is supported by a set of cost models that estimate the externalities as a function of a multivariate vector of traffic flows. These models were estimated using Taylor series expansions of the output obtained from traffic simulations of a hypothetical test case. Of importance to the paper is the total travel time function estimated using this approach that expresses total travel time as a multivariate function of the traffic volumes. The formulation presented in the paper is then applied to a variety of scenarios to gain insight into the optimality of current toll policies. The optimal tolls are computed for two different cases: independent tolls, and tolls proportional to passenger car equivalencies (PCE).The numerical results clearly show that setting tolls proportional to PCEs leads to lower values of welfare that are on average 15% lower than when using independent tolls, though, in some cases the total welfare could be up to 33% lower. This is a consequence of two factors. First, the case of independent tolls has more degrees of freedom than the case of tolls proportional to PCEs. Second, tolls proportional to PCEs do not account for externalities other than congestion, which is likely to lead to lower welfare values.The analytical formulations and numerical results indicate that, because the total travel time is a non-linear function of the traffic volumes, the marginal social costs and thus the optimal congestion tolls also depend on the traffic volumes for each vehicle class. As a result of this, for the relatively low volumes of truck traffic observed in real life, the optimal congestion tolls for trucks could indeed be either lower or about the same as for passenger cars. This stand in sharp contrast with what is implied in the use of PCEs, i.e., that the contribution to congestion are constant. This latter assumption leads to optimal truck congestion tolls that are always proportional to the PCE values.The comparison of the toll ratios (truck tolls divided by passenger car tolls) for both observed and optimal conditions suggests that the tolls for small trucks are about the right level, maybe a slightly lower than optimal. However, the analysis of the toll ratio for large trucks seems to indicate a significant overcharge. The estimates show that the average observed toll ratio for large trucks is even higher than the maximum optimal toll ratio found in the numerical experiments. This suggests that the tolls for large trucks are set on the basis of revenue generation principles while the passenger car tolls are being set based on a mild form of welfare maximization. This leads to a suboptimal cross-subsidization of passenger car traffic in detriment of an important sector of the economy.     

Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance

Traffic counts on network links constitute an information source on travel demand which is easy to collect, cheap and repeatable. Many models proposed in recent years deal with the use of traffic counts to estimate Origin/Destination (O/D) trip matrices under different assumptions on the type of "a-priori" information available on the demand (surveys, outdated estimates, models, etc.) and the type of network and assignment mapping (see Cascetta & Nguyen 1988). Less attention has been paid to the possibility of using traffic counts to estimate the parameters of demand models. In this case most of the proposed methods are relative to particular demand model structures (e.g. gravity-type) and the statistical analysis of estimator performance is not thoroughly carried out. In this paper a general statistical framework defining Maximum Likelihood, Non Linear Generalized Least Squares (NGLS) and Bayes estimators of aggregated demand model parameters combining counts-based information with other sources (sample or a priori estimates) is proposed first, thus extending and generalizing previous work by the authors (Cascetta & Russo 1992). Subsequently a solution algorithm of the projected-gradient type is proposed for the NGLS estimator given its convenient theoretical and computational properties. The algorithm is based on a combination of analytical/numerical derivates in order to make the estimator applicable to general demand models. Statistical performances of the proposed estimators are evaluated on a small test network through a Monte Carlo method by repeatedly sampling "starting estimates" of the (known) parameters of a generation/distribution/modal split/assignment system of models. Tests were carried out assuming different levels of "quality" of starting estimates and numbers of available counts. Finally NGLS estimator was applied to the calibration of the described model system on the network of a real medium-size Italian town using real counts with very satisfactory results in terms of both parameter values and counted flows reproduction.     

Temporal and spatial variation in allocating annual traffic activity across an urban region and implications for air quality assessments

Patterns of traffic activity, including changes in the volume and speed of vehicles, vary over time and across urban areas and can substantially affect vehicle emissions of air pollutants. Time-resolved activity at the street scale typically is derived using temporal allocation factors (TAFs) that allow the development of emissions inventories needed to predict concentrations of traffic-related air pollutants. This study examines the spatial and temporal variation of TAFs, and characterizes prediction errors resulting from their use. Methods are presented to estimate TAFs and their spatial and temporal variability and used to analyze total, commercial and non-commercial traffic in the Detroit, Michigan, U.S. metropolitan area. The variability of total volume estimates, quantified by the coefficient of variation (COV) representing the percentage departure from expected hourly volume, was 21%, 33%, 24% and 33% for weekdays, Saturdays, Sundays and holidays, respectively. Prediction errors mostly resulted from hour-to-hour variability on weekdays and Saturdays, and from day-to-day variability on Sundays and holidays. Spatial variability was limited across the study roads, most of which were large freeways. Commercial traffic had different temporal patterns and greater variability than non-commercial vehicle traffic, e.g., the weekday variability of hourly commercial volume was 28%. The results indicate that TAFs for a metropolitan region can provide reasonably accurate estimates of hourly vehicle volume on major roads. While vehicle volume is only one of many factors that govern on-road emission rates, air quality analyses would be strengthened by incorporating information regarding the uncertainty and variability of traffic activity.     

Required traffic micro‐simulation runs for reliable multivariate performance estimates

Previous methods to calculate the minimum number of traffic micro‐simulation runs do not consider multiple measures of performance simultaneously at an overall confidence level, which can lead to unreliable simulation outputs. This paper describes new methodologies for calculating the minimum number of traffic micro‐simulation runs for multivariate estimates at an overall confidence level. Simultaneous confidence intervals obtained from multiple comparisons in statistical theory such as the Bonferroni inequality and simultaneous confidence interval method are used to estimate multiple measures of performance with allowable errors at an overall confidence level. Measures of performance can be means and standard deviations. Results of numerical analysis based on an example corridor suggest that the proposed methods provide improved means of assessing statistical accuracy of multiple measures of performance. Results also indicate that the minimum number of runs is influenced by not only the sample size issue but also the complexity of the traffic system. Copyright © 2015 John Wiley & Sons, Ltd.     

Connected vehicle penetration rate for estimation of arterial measures of effectiveness

The Connected Vehicle (CV) technology is a mobile platform that enables a new dimension of data exchange among vehicles and between vehicles and infrastructure. This data source could improve the estimation of Measures of Effectiveness (MOEs) for traffic operations in real-time, allowing to perfectly monitor traffic states after being fully adopted. However, as with any novel technology, the CV adoption will be a gradual process. This research focuses on determining minimum CV technology penetration rates that would guarantee accurate MOE estimates on signalized arterials. First, we present estimation methods for various MOEs such as average speed, number of stops, acceleration noise, and delay, followed by an initial assessment of the penetration rates required to accurately estimate them in undersaturated and oversaturated conditions. Next, we propose a methodology to determine the minimum CV market penetration rates to guarantee accurate MOE estimates as a function of traffic conditions, signal settings, sampling duration, and the MOE variability. A correction factor is also provided to account for small vehicle populations where sampling is done without replacement. The methodology is tested in a simulated segment of the San Pablo Avenue arterial in Berkeley, CA. The outcomes show that the minimum penetration rate required can be estimated within 1% for most MOEs under a wide range of traffic conditions. The proposed methodology can be used to determine if MOE estimates obtained with a portion of CV equipped vehicles can yield accurate enough results. The methodology could also be used to develop and assess control strategies towards improved arterial traffic operations.     

Origin-destination pattern estimation based on trajectory reconstruction using automatic license plate recognition data

Origin-destination (OD) pattern estimation is a vital step for traffic simulation applications and active urban traffic management. Many methods have been proposed to estimate OD patterns based on different data sources, such as GPS data and automatic license plate recognition (ALPR) data. These data can be used to identify vehicle IDs and estimate their trajectories by matching vehicles identified by different sensors across the network. OD pattern estimation using ALPR data remains a challenge in real-life applications due to the difficulty in reconstructing vehicle trajectories. This paper proposes an offline method for historical OD pattern estimation based on ALPR data. A particle filter is used to estimate the probability of a vehicle’s trajectory from all possible candidate trajectories. The initial particles are generated by searching potential paths in a pre-determined area based on the time geography theory. Then, the path flow estimation process is conducted through dividing the reconstructed complete trajectories of all detected vehicles into multiple trips. Finally, the OD patterns are estimated by adding up the path flows with the same ODs. The proposed method was implemented on a real-world traffic network in Kunshan, China and verified through a calibrated microscopic traffic simulation model. The results show that the MAPEs of the OD estimation are lower than 19%. Further investigation shows that there exists a minimum required ALPR sampling rate (60% in the test network) for accurately estimating the OD patterns. The findings of this study demonstrate the effectiveness of the proposed method in OD pattern estimation.     

A generalized sensor location model for the estimation of network origin–destination matrices

This study examined the network sensor location problem by using heterogeneous sensor information to estimate link-based network origin–destination (O–D) demands. The proposed generalized sensor location model enables different sensors’ traffic monitoring capabilities to be used efficiently and the optimal number and deployment locations of both passive- and active-type sensors to be determined simultaneously without path enumeration. The proposed sensor location model was applied to solve the network O–D demand estimation problem. One unique aspect of the proposed model and solution algorithms is that they provide satisfactory network O–D demand estimates without requiring unreasonable assumptions of known prior information on O–D demands, turning proportions, or route choice probabilities. Therefore, the proposed model and solution algorithms can be practically used in numerous offline transportation planning and online traffic operation applications.     

Development of a delay model for unsignalized intersections applicable to traffic assignment

Abstract

This paper develops a model for estimating unsignalized intersection delays which can be applied to traffic assignment (TA) models. Current unsignalized intersection delay models have been developed mostly for operational purposes, and demand detailed geometric data and complicated procedures to estimate delay. These difficulties result in unsignalized intersection delays being ignored or assumed as a constant in TA models.

Video and vehicle license plate number recognition methods are used to collect traffic volume data and to measure delays during peak and off-peak traffic periods at four unsignalized intersections in the city of Tehran, Iran. Data on geometric design elements are measured through field surveys. An empirical approach is used to develop a delay model as a function of influencing factors based on 5- and 15-min time intervals. The proposed model estimates delays on each approach based on total traffic volumes, rights-of-way of the subject approach and the intersection friction factor. The effect of conflicting traffic flows is considered implicitly by using the intersection friction factor. As a result, the developed delay model guarantees the convergence of TA solution methods.

A comparison between delay models performed using different time intervals shows that the coefficients of determination, R ², increases from 43.2% to 63.1% as the time interval increases from 5- to 15-min. The US Highway Capacity Manual (HCM) delay model (which is widely used in Iran) is validated using the field data and it is found that it overestimates delay, especially in the high delay ranges.     

Estimating traffic volumes on intercity road locations using roadway attributes,socioeconomic features and other work-related activity characteristics

Traffic volume data are key inputs to many applications in highway design and planning. But these data are collected in only a limited number of road locations due to the cost involved. This paper presents an approach for estimating daily and hourly traffic volumes on intercity road locations combining clustering and regression modelling techniques. With the aim of applying the procedure to any road location, it proposes the use of roadway attributes and socioeconomic characteristics of nearby cities as explanatory variables, together with a set of previously discovered patterns with the hourly traffic percent distribution. Test results show that the proposed approach significantly produces accurate estimates of daily volumes for most locations. The accuracy at hourly level is a bit more reduced but, for periods when traffic is significant, more than half of the estimates are within 20% of absolute percentage error. Moreover, the main peak period is approximately identified for most cases. These findings together with its great applicability make this approach attractive for planners when no traffic data are available and an estimate is helpful.     

Evaluation of variable speed limits to improve traffic safety

This paper examines automated control strategies of variable speed limits that aim at reducing crash potential on instrumented freeways. A real-time crash prediction model was developed to estimate crash potential based on short-term variation of traffic flow characteristics. A microscopic traffic simulation model was used to realistically simulate changes in traffic conditions as an effect of variable speed limits and combined with the crash prediction model for the evaluation of control logics. Within this integrated evaluation framework, the study investigated the effect of strategy control factors on the crash potential reduction and total travel time. The study results indicated that variable speed limits could reduce crash potential by 5–17%, by temporarily reducing speed limits during risky traffic conditions when crash potential exceeded the pre-specified threshold.     

Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.     

Estimating median velocity instead of mean velocity at single loop detectors

Loop detectors are the preeminent vehicle detector for freeway traffic surveillance. Although single loops have been used for decades, debate continues on how to interpret the measurements. Many researchers have sought better estimates of velocity from single loops. The preceding work has emphasized techniques that use many samples of aggregate flow and occupancy to reduce the estimation error. Although rarely noted, these techniques effectively seek to reduce the bias due to long vehicles in measured occupancy. This paper presents a different approach, using a new aggregation methodology to estimate velocity and reduce the impact of long vehicles in the original traffic measurements. In contrast to conventional practice, the new estimate significantly reduces velocity estimation errors when it is not possible to control for a wide range of vehicle lengths.