Path flow estimator for planning applications in small communities

This paper presents an alternative planning framework to model and forecast network traffic for planning applications in small communities, where limited resources debilitate the development and applications of the conventional four-step travel demand forecasting model. The core idea is to use the Path Flow Estimator (PFE) to estimate current and forecast future traffic demand while taking into account of various field and planning data as modeling constraints. Specifically, two versions of PFE are developed: a base year PFE for estimating the current network traffic conditions using field data and planning data, if available, and a future year PFE for predicting future network traffic conditions using forecast planning data and the estimated base year origin–destination trip table as constraints. In the absence of travel survey data, the proposed method uses similar data (traffic counts and land use data) as a four-step model for model development and calibration. Since the Institute of Transportation Engineers (ITE) trip generation rates and Highway Capacity Manual (HCM) are both utilized in the modeling process, the analysis scope and results are consistent with those of common traffic impact studies and other short-range, localized transportation improvement programs. Solution algorithms are also developed to solve the two PFE models and integrated into a GIS-based software called Visual PFE. For proof of concept, two case studies in northern California are performed to demonstrate how the tool can be used in practice. The first case study is a small community of St. Helena, where the city’s planning department has neither an existing travel demand model nor the budget for developing a full four-step model. The second case study is in the city of Eureka, where there is a four-step model developed for the Humboldt County that can be used for comparison. The results show that the proposed approach is applicable for small communities with limited resources.     

Temporal disaggregation of travel demand for high resolution emissions inventories

The commonly used photochemical air quality model, the Urban Airshed Model (UAM), requires emission estimates with grid-based, hourly resolution. In contrast, travel demand models, used to simulate the travel activity model inputs for the transportation-related emissions estimation, typically only provide traffic volumes for a specific travel period (e.g. the a.m. and p.m. peak periods). A few transportation agencies have developed procedures to allocate period-based travel demand data into hourly emission inventories for regional grid cells. Because there was no theoretical framework for disaggregating period-based volumes to hourly volumes, application of these procedures frequently relied upon a single hypothetical hourly distribution of travel volumes. This study presents a new theoretical modeling framework that integrates traffic count data and travel demand model link volume estimates to derive intra-period hourly volume estimates by trip purpose. We propose a new interpretation of the model coefficients and define hourly allocation factors by trip purpose. These allocation factors can be used to disaggregate the travel demand model ‘period-based’ simulation volumes into hourly resolution, thereby improving grid-based, hourly emission estimates in the UAM.     

The estimation of a time-dependent OD trip table with vehicle trajectory samples

Abstract

In this paper we discuss a dynamic origin–destination (OD) estimation problem that has been used for identifying time-dependent travel demand on a road network. Even though a dynamic OD table is an indispensable data input for executing a dynamic traffic assignment, it is difficult to construct using the conventional OD construction method such as the four-step model. For this reason, a direct estimation method based on field traffic data such as link traffic counts has been used. However, the method does not account for a logical relationship between a travel demand pattern and socioeconomic attributes. In addition, the OD estimation method cannot guarantee the reliability of estimated results since the OD estimation problem has a property named the ‘underdetermined problem.’ In order to overcome such a problem, the method developed in this paper makes use of vehicle trajectory samples with link traffic counts. The new method is applied to numerical examples and shows promising capability for identifying a temporal and spatial travel demand pattern.     

Deciding whether and how to improve statewide travel demand models based on transportation planning application needs

Many states in the USA have developed statewide travel demand models for transportation planning at the state level and along intercity corridors. Travel demand models at mega-region and provincial levels are also widely used in Europe and Asia. With modern transportation planning applications requiring enhanced model capabilities, many states are considering improving their four-step statewide demand models. This paper synthesizes representative statewide models developed with traditional four-step, advanced four-step, and integrated micro-simulation methods. The focus of this synthesis study is as much on model applications and data requirements as on modeling methods. An incremental model improvement approach toward advanced statewide models is recommended. Review findings also suggest model improvement activities should be justified by planning application needs. For statewide model improvement plans to be successful and financially sustainable, the return on model improvement investment needs to be demonstrated by timely applications that rely on improved model capabilities.     

Modelling the effects of road pricing on traffic using ERP traffic data

Singapore’s Electronic Road Pricing (ERP) system involves time-variable charges which are intended to spread the morning traffic peak. The charges are revised every three months and thus induce regular motorists to re-think their travel decisions. ERP traffic data, captured by the system, provides a valuable source of information for studying motorists’ travel behaviour. This paper proposes a new modelling methodology for using these data to forecast short-term impacts of rate adjustment on peak period traffic volumes. Separate models are developed for different categories of vehicles which are segmented according to their demand elasticity with respect to road pricing. A method is proposed for estimating the maximum likelihood value of preferred arrival time (PAT) for each vehicle’s arrivals at a particular ERP gantry under different charging conditions. Iterative procedures are used in both model calibration and application. The proposed approach was tested using traffic datasets recorded in 2003 at a gantry located on Singapore’s Central Expressway (CTE). The model calibration and validation show satisfactory results.     

Integrating a traffic router and microsimulator into a land use and travel demand model

Abstract

This paper describes one of the first known attempts at integrating a dynamic and disaggregated land-use model with a traffic microsimulator and compares its predictions of land use to those from an integration of the same land-use model with a more traditional four-step travel demand model. For our study area of Chittenden County, Vermont, we used a 40-year simulation beginning in 1990. Predicted differences in residential units between models for 2030 broken down by town correlated significantly with predicted differences in accessibility. The two towns with the greatest predicted differences in land use and accessibility are also the towns that currently have the most severe traffic bottlenecks and poorest route redundancy. Our results suggest that this particular integration of a microsimulator with a disaggregated land-use model is technically feasible, but that in the context of an isolated, small metropolitan area, the differences in predicted land use are small.     

A comparative analysis of traditional four-step and activity-based travel demand modeling: a case study of Tampa,Florida

Activity-based travel demand modeling (ABTDM) has often been viewed as an advanced approach, due to its higher fidelity and better policy sensitivity. However, a review of the literature indicates that no study has been undertaken to investigate quantitatively the differences and accuracy between an ABTDM approach and a traditional four-step travel demand model. In this paper we provide a comparative analysis against each step – trip generation, trip distribution, mode split, and network assignment – between an ABTDM developed using travel diary data from the Tampa Bay Region in Florida and the Tampa Bay Regional Planning Model (TBRPM), an existing traditional four-step model for the same area. Results show salient differences between the TBRPM and the ABTDM, in terms of modeling performance and accuracy, in each of the four steps. For example, trip production rates calculated from the travel diary data are found to be either double or a quarter less than those used in the TBRPM. On the other hand, trip attraction rates computed from activity-based travel simulations are found to be either more than double or one tenth less than those used in the TBRPM. The trip distribution curves from the two models are similar, but both average and peak trip lengths of the two are significantly different. Mode split analyses show that the TBRPM may underestimate driving trips and fail to capture any usage of alternative modes, such as taxi and nonmotorized (e.g., walking and bicycling) modes. In addition, the ABTDMs are found to be less capable of reproducing observed traffic counts when compared to the TBRPM, most likely due to not considering external and through trips. The comparative results presented can help transportation engineers and planners better understand the strengths and weaknesses of the two types of model and this should assist decision-makers in choosing a better modeling tool for their planning initiatives.     

Using observed traffic volumes to improve fine-grained regional emissions estimation

When translating travel demand model output to photochemical model input, period-based network assignment volumes must be converted to gridded-hourly vehicle emissions. A post-processor, such as the California Direct Travel Impact Model (DTIM2), is frequently used to disaggregate the period-based travel demand assignments to the fine grained spatial and temporal resolution required by the photochemical models. A recent theoretical enhancement proposed refining the temporal and spatial resolutions of travel demand model predictions using observed count data. This method provides a technique for disaggregating the period-based travel demand model assignments (e.g., AM peak, PM peak) into the hourly summaries required by most photochemical model (Lin and Niemeier, 1997). In this study we present a methodological framework for applying the new theory and discuss the results of a large-scale application empirical comparison between the standard and proposed methods for estimating regional mobile emissions in Sacramento, California. The standard method produced slightly higher estimates of daily emissions (about 1%) when compared to the emissions estimated using observed count data. However, the two approaches produced hourly emissions estimates that differed by as much as 15% in some hours.     

Impact of passing-climbing lanes on traffic flow on upgrades

This study evaluates a new approach for reducing delay, and consequently improving level of service and safety on long upgrades on two-lane rural roads. This is the systematic provision of overtaking lanes, termed passing-climbing lanes (PCL), to improve traffic flow, safety, and capacity. The traffic impact of such lanes is analyzed for various grades, traffic volumes, and lane configurations by means of a simulation model developed for this study. Results show that this concept could provide substantial flow benefits—reduction in delay and in passenger-car platooning—with implications for better safety. Although the reduction in delay is found to be more pronounced as volume increases, these results may be obtained even with a small percentage of passing-climbing lanes. A model predicting average relative delay, formulated and calibrated on the basis of the simulation output, explains 95% of the observed variability. The economic advantages of the concept in optimizing the distribution of a limited budget among several sites, in staging construction over several years, and in adapting highway investment to traffic-demand variations are also discussed.     

How much is too much for tolled road users: Toll saturation and the implications for car commuting value of travel time savings?

The current practice of forecasting the demand for new tolled roads typically assumes that car users are prepared to pay a higher toll for a shorter journey, and they will keep doing so as long as the toll cost is not higher than their current value of travel time savings. Practice ignores the possibility that there could be a point when motorists stop driving on toll roads due to a toll budget constraint. The unconstrained toll budget assumption may be valid in networks where the addition of a new toll road does not result in a binding budget constraint that car users may have for using toll roads (although it could also be invoked for existing tolled routes through a reduction in use of a tolled route). In a road network like Sydney which offers a growing number of (linked) tolled roads, the binding budget constraint may be invoked, and hence including additional toll links might in turn reduce the car users’ willingness to pay for toll roads to save the same amount of travel time. When this occurs, car users are said to reach a toll saturation point (or threshold) and begin to consider avoiding one or more toll roads. Whilst toll saturation has important implications for demand forecasting and planning of toll roads, this type of behaviour has not been explored in the literature. We investigate the influence that increasing toll outlays has on preferences of car commuters to use one or more tolled roads as the number of tolled roads increases. The Sydney metropolitan area offers a unique laboratory to test this phenomenon, with nine tolled roads currently in place and another five in planning. The evidence supports the hypothesis that the value of travel time savings decreases as a consequence of toll saturation.     

Impacts of highly automated vehicles on travel demand: macroscopic modeling methods and some results

Automated vehicles (AV) will change transport supply and influence travel demand. To evaluate those changes, existing travel demand models need to be extended. This paper presents ways of integrating characteristics of AV into traditional macroscopic travel demand models based on the four-step algorithm. It discusses two model extensions. The first extension allows incorporating impacts of AV on traffic flow performance by assigning specific passenger car unit factors that depend on roadway type and the capabilities of the vehicles. The second extension enables travel demand models to calculate demand changes caused by a different perception of travel time as the active driving time is reduced. The presented methods are applied to a use case of a regional macroscopic travel demand model. The basic assumption is that AV are considered highly but not fully automated and still require a driver for parts of the trip. Model results indicate that first-generation AV, probably being rather cautious, may decrease traffic performance. Further developed AV will improve performance on some parts of the network. Together with a reduction in active driving time, cars will become even more attractive, resulting in a modal shift towards car. Both circumstances lead to an increase in time spent and distance traveled.

     

The changing accuracy of traffic forecasts

Researchers have improved travel demand forecasting methods in recent decades but invested relatively little to understand their accuracy. A major barrier has been the lack of necessary data. We compiled the largest known database of traffic forecast accuracy, composed of forecast traffic, post-opening counts and project attributes for 1291 road projects in the United States and Europe. We compared measured versus forecast traffic and identified the factors associated with accuracy. We found measured traffic is on average 6% lower than forecast volumes, with a mean absolute deviation of 17% from the forecast. Higher volume roads, higher functional classes, shorter time spans, and the use of travel models all improved accuracy. Unemployment rates also affected accuracy—traffic would be 1% greater than forecast on average, rather than 6% lower, if we adjust for higher unemployment during the post-recession years (2008 to 2014). Forecast accuracy was not consistent over time: more recent forecasts were more accurate, and the mean deviation changed direction. Traffic on projects that opened from the 1980s through early 2000s was higher on average than forecast, while traffic on more recent projects was lower on average than forecast. This research provides insight into the degree of confidence that planners and policy makers can expect from traffic forecasts and suggests that we should view forecasts as a range of possible outcomes rather than a single expected outcome.

     

Developing an activity-based trip generation model for small/medium size planning agencies

The primary shortcoming of traditional four-step models is that they cannot capture derived travel demand behaviors. However, travel demand modeling (TDM) is an essential input for urban transportation planning. TDM needs to be highly precise and accurate by integrating the accurate base year estimation along with suitable alternatives. Currently, activity-based models (ABMs) have been developed mostly for large metropolitan planning organizations (MPO), whereas smaller/medium-sized MPOs typically lack these models. The main reason for this disparity in ABM development is the complexity of the models and the cost and data requirements needed. We posit however that smaller MPOs could develop ABMs from traditional travel surveys. Therefore, the specific aim of this paper is to develop a probabilistic home-based destination activity trip generation model considering travel time behavior. Results show that the developed model can significantly capture the actual number of trip generations.     

The analysis of travel and emission impacts of travel demand management strategies using activity-based models

This paper demonstrates, tests and shows the value of activity-based travel demand models and household sample enumeration forecasting techniques in evaluating the transportation and air quality impacts of travel demand management strategies. Using data from the Portland, Oregon metropolitan area, three transportation policies were evaluated both individually and in combination: transit improvements, pricing, and telecommunications. The activity-based models used in this testing represents a significant improvement to today's "four-step" sequential model systems by providing a deeper insight into the individual decision making process in response to transportation policies. A wider range of impacts is predicted, and indirect effects as well as synergistic effects of such policies are taken into consideration. These models are capable of providing the information needed to improve the linkage of transportation models with emissions and air quality analysis methodologies by improving the prediction of variables that are important to accurately estimating emissions and air quality impacts of transportation actions.     

An intersection turning movement estimation procedure based on path flow estimator

Estimation of intersection turning movements is one of the key inputs required for a variety of transportation analysis, including intersection geometric design, signal timing design, traffic impact assessment, and transportation planning. Conventional approaches that use manual techniques for estimation of turning movements are insensitive to congestion. The drawbacks of the manual techniques can be amended by integrating a network traffic model with a computation procedure capable of estimating turning movements from a set of link traffic counts and intersection turning movement counts. This study proposes using the path flow estimator, originally used to estimate path flows (hence origin–destination flows), to derive not only complete link flows, but also turning movements for the whole road network given some counts at selected roads and intersections. Two case studies using actual traffic counts are used to demonstrate the proposed intersection turning movement estimation procedure. Copyright © 2010 John Wiley & Sons, Ltd.     

Characteristics of truck traffic in Singapore

This paper presents the results of a project conducted to study the characteristics of truck traffic in Singapore. Detailed traffic surveys recording counts of vehicles by axle-configuration were performed at 219 sites over a period of nearly two years. The surveys covered 5 different road classes, namely expressways, arterials, collectors, industrial roads and local roads. It was found that the time distribution of truck travel were not the same among the five road classes. The peaking characteristics of truck traffic were less pronounced compared to passenger car traffic. The peak hour truck volume varied from 67.0% to 9.7% of the daily truck traffic as compared to 13.8% for passenger car traffic. The lane distribution pattern of truck traffic was studied in detail by road class, and was found to be a function of total directional traffic volume, total directional truck volume and the number of traffic lanes. Composition analysis was also carried out to study the lane use characteristics of single- and multiple-unit trucks.     

Bridge and tunnel toll elasticities in New York

In 1992, the authors carried out a statistical analysis of Triborough Bridge and Tunnel Authority (TBTA) crossings in New York City, to determine the impact of toll increases on traffic volumes and revenue. Using twelve years of monthly time-series data, we developed a set of multiple regression models that estimated traffic volumes on each TBTA bridge and tunnel as a function of the toll level and other explanatory variables. In most cases, the estimated toll elasticities were negative and much less than 1.0 in absolute value; the median toll elasticity for automobiles was found to be –0.10. Our finding that automobile travel demand is highly inelastic with respect to toll rates is consistent with most previous travel demand studies.     

Short-Term Prediction of Travel Time using Neural Networks on an Interurban Highway

The main purpose of this study was to investigate the predictability of travel time with a model based on travel time data measured in the field on an interurban highway. Another purpose was to determine whether the forecasts would be accurate enough to implement the model in an actual online travel time information service. The study was carried out on a 28-kilometre-long rural two-lane road section where traffic congestion was a problem during weekend peak hours. The section was equipped with an automatic travel time monitoring and information system. The prediction models were made as feedforward multilayer perceptron neural networks. The main results showed that the majority of the forecasts were close to the actual measured values. Consequently, use of the prediction model would improve the quality of travel time information based directly on the sum of the latest measured travel times.     

An impact analysis of traffic image information system on driver travel choice

A driver is one of the main components in a transportation system that influences the effectiveness of any active demand management (ADM) strategies. As such, the understanding on driver behavior and their travel choice is crucial to ensure the successful implementation of ADM strategies in alleviating traffic congestion, especially in city centres. This study aims to investigate the impact of traffic information dissemination via traffic images on driver travel choice and decision. A relationship of driver travel choice with respect to their perceived congestion level is developed by an integrated framework of genetic algorithm–fuzzy logic, being a new attempt in driver behavior modeling. Results show that drivers consider changing their travel choice when the perceived congestion level is medium, in which changing departure time and diverting to alternative roads are two popular choices. If traffic congestion escalates further, drivers are likely to cancel their trip. Shifting to public transport system is the least likely choice for drivers in an auto-dependent city. These findings are important and useful to engineers as they are required to fully understand driver (user) sensitivity to traffic conditions so that relevant active travel demand management strategies could be implemented successfully. In addition, engineers could use the relationships established in this study to predict drivers’ response under various traffic conditions when carrying out modeling and impact studies.     

Sensitivity-based uncertainty analysis of a combined travel demand model

Travel demand forecasting is subject to great uncertainties. A systematic uncertainty analysis can provide insights into the level of confidence on the model outputs, and also identify critical sources of uncertainty for enhancing the robustness of the travel demand model. In this paper, we develop a systematic framework for quantitative uncertainty analysis of a combined travel demand model (CTDM) using the analytical sensitivity-based method. The CTDM overcomes limitations of the sequential four-step procedure since it is based on a single unifying rationale. The analytical sensitivity-based method requires less computational effort than the sampling-based method. Meanwhile, the uncertainties stemming from inputs and parameters can be treated separately so that the individual and collective effects of uncertainty on the outputs can be clearly assessed and quantified. Numerical examples are finally used to demonstrate the proposed sensitivity-based uncertainty analysis method for the CTDM.