Managing transportation during highway reconstruction: A recommended process

Major highway reconstruction can cause significant disruptions to existing travel patterns and economic activity. Reducing these impacts on travelers, shippers, businesses and residents requires that innovative and effective transportation management actions be developed and implemented. This paper reports the major findings and recommendations of a research study on managing transportation during highway, reconstruction. The primary objectives of the study were

Urban university campus transportation and parking planning through a dynamic traffic simulation and assignment approach

Abstract

Many urban university campuses are considered major trip attractors. Considering the multimodal and complex nature of university campus transportation planning and operation, this paper proposes a dynamic traffic simulation and assignment analysis approach and demonstrates how such a methodology can be successfully applied. Central to the research is the estimation of trip origin–destinations and the calibration of a parking lot choice model. Dynamic simulation is utilized to simulate multiple modes of transportation within the transportation network while further assigning these modes with respect to various mode-specific roadway accessibilities. A multiple vehicle-class simulation analysis for planning purposes becomes a critical capability to predict how faculty and staff who once parked within the campus core choose other nearby alternate parking lots. The results highlight the effectiveness of the proposed approach in providing integrated and reliable solutions for challenging questions that face urban university campus planners and local transportation jurisdictions.     

Hybrid artificial neural network and locally weighted regression models for lane-based short-term urban traffic flow forecasting

ABSTRACT

In recent years, there has been considerable research interest in short-term traffic flow forecasting. However, forecasting models offering a high accuracy at a fine temporal resolution (e.g. 1 or 5?min) and lane level are still rare. In this study, a combination of genetic algorithm, neural network and locally weighted regression is used to achieve optimal prediction under various input and traffic settings. The genetically optimized artificial neural network (GA-ANN) and locally weighted regression (GA-LWR) models are developed and tested, with the former forecasting traffic flow every 5-min within a 30-min period and the latter for forecasting traffic flow of a particular 5-min period of each for four lanes of an urban arterial road in Beijing, China. In particular, for morning peak and off-peak traffic flow prediction, the GA-ANN 5-min traffic flow model results in average errors of 3–5% and most 95th percentile errors of 7–14% for each of the four lanes; for the peak and off-peak time traffic flow predictions, the GA-LWR 5-min traffic flow model results in average errors of 2–4% and most 95th percentile errors are lower than 10% for each of the four lanes. When compared to previous models that usually offer average errors greater than 6–15%, such empirical findings should be of interest to and instrumental for transportation authorities to incorporate in their city- or state-wide Advanced Traveller Information Systems (ATIS).     

Uncertain socioeconomic projections used in travel demand and emissions models: could plausible errors result in air quality nonconformity?

A sensitivity analysis of plausible errors in population, employment, fuel price, and income projections is conducted using the travel demand and emissions models of the Sacramento, CA, USA, region for their transportation plan. The results of the analyses indicate that plausible error ranges for household income and fuel prices are not a significant source of uncertainty with respect to the region's travel demand and emissions projections. However, plausible errors in population and employment projections (within approximately one standard deviation) may result in the region's transportation plan not meeting the conformity test for nitrogens of oxides (NO_x) in the year 2005 (i.e., an approximately 16% probability). This outcome is also possible in the year 2015 but less likely (within approximately two standard deviations or a 2.5% probability). Errors in socioeconomic projections are only one of many sources of error in travel demand and emissions models. These results have several policy implications. First, regions like Sacramento that meet their conformity tests by a very small margin should rethink new highway investment and consider contingency transportation plans that incorporate more aggressive emissions reduction policies. Second, regional transportation planning agencies should conduct sensitivity analyses as part of their conformity analysis to make explicit significant uncertainties in the methods and to identify the probability of their transportation plan not conforming. Third, the US Environmental Protection Agency (EPA) should clarify the interpretation of “demonstrate” conformity of transportation plans; that is, specify the level of certainty that it considers a sufficient demonstration of conformity.     

A study of traveler behavior under traffic information-provided conditions in the Beijing area

ABSTRACT

The quality of traffic information has become one of the most important factors that can affect the distribution of urban and highway traffic flow by changing the travel route, transportation mode, and travel time of travelers and trips. Past research has revealed traveler behavior when traffic information is provided. This paper summarizes the related study achievements from a survey conducted in the Beijing area with a specially designed questionnaire considering traffic conditions and the provision of traffic information services. With the survey data, a Logit model is estimated, and the results indicate that travel time can be considered the most significant factor that affects highway travel mode choice between private vehicles and public transit, whereas trip purpose is the least significant factor for private vehicle usage for both urban and highway travel.     

Identifying optimal data aggregation interval sizes for link and corridor travel time estimation and forecasting

With the recent increase in the deployment of ITS technologies in urban areas throughout the world, traffic management centers have the ability to obtain and archive large amounts of data on the traffic system. These data can be used to estimate current conditions and predict future conditions on the roadway network. A general solution methodology for identifying the optimal aggregation interval sizes for four scenarios is proposed in this article: (1) link travel time estimation, (2) corridor/route travel time estimation, (3) link travel time forecasting, and (4) corridor/route travel time forecasting. The methodology explicitly considers traffic dynamics and frequency of observations. A formulation based on mean square error (MSE) is developed for each of the scenarios and interpreted from a traffic flow perspective. The methodology for estimating the optimal aggregation size is based on (1) the tradeoff between the estimated mean square error of prediction and the variance of the predictor, (2) the differences between estimation and forecasting, and (3) the direct consideration of the correlation between link travel time for corridor/route estimation and forecasting. The proposed methods are demonstrated using travel time data from Houston, Texas, that were collected as part of the automatic vehicle identification (AVI) system of the Houston Transtar system. It was found that the optimal aggregation size is a function of the application and traffic condition.

Distributed MPC for cooperative highway driving and energy-economy validation via microscopic simulations

Traffic congestion and energy issues have set a high bar for current ground transportation systems. With advances in vehicular communication technologies, collaborations of connected vehicles have becoming a fundamental block to build automated highway transportation systems of high efficiency. This paper presents a distributed optimal control scheme that takes into account macroscopic traffic management and microscopic vehicle dynamics to achieve efficiently cooperative highway driving. Critical traffic information beyond the scope of human perception is obtained from connected vehicles downstream to establish necessary traffic management mitigating congestion. With backpropagating traffic management advice, a connected vehicle having an adjustment intention exchanges control-oriented information with immediately connected neighbors to establish potential cooperation consensus, and to generate cooperative control actions. To achieve this goal, a distributed model predictive control (DMPC) scheme is developed accounting for driving safety and efficiency. By coupling the states of collaborators in the optimization index, connected vehicles achieve fundamental highway maneuvers cooperatively and optimally. The performance of the distributed control scheme and the energy-saving potential of conducting such cooperation are tested in a mixed highway traffic environment by the means of microscopic simulations.     

Alternative methods for estimating full marginal costs of highway transportation

This paper presents various methods of estimating the full marginal cost (FMC) of highway passenger transportation. First, the computation of FMC is performed using the marginal cost functions, most of which were developed by Ozbay et al. [Ozbay, K., Bartin, B., Berechman, J., 2001. Estimation and evaluation of full marginal costs of highway transportation in New Jersey. Journal of Transportation and Statistics 4 (1)]. FMC is defined and calculated as “total cost per trip” as explained in this paper. However, in multiple origin-destination and multiple route highway networks, the practical application of the network-wide FMC concept is complicated. These issues are addressed in detail in this paper. Therefore, in the second method, a multiple route based FMC approach is proposed for a given origin-destination pair in the network. It is observed that the marginal values of different paths vary as much as 28%. Third, a comparison of FMC estimation results of two distinct measurement tools is presented. The FMC estimation is performed between a selected OD pair using the static transportation planning software output (TransCAD). The same analysis is repeated using the stochastic traffic simulation software output (PARAMICS). The differences in FMC values estimated by static transportation planning software and microscopic traffic simulation software are discussed.     

An empirical study of simulation-based dynamic traffic assignment procedures

Abstract

Under Intelligent Transportation Systems (ITS), real-time operations of traffic management measures depend on long-term planning results, such as the origin–destination (OD) trip distribution; however, results from current planning procedures are unable to provide fundamental data for dynamic analysis. In order to capture dynamic traffic characteristics, transportation planning models should play an important role to integrate basic data with real-time traffic management and control. In this paper, a heuristic algorithm is proposed to establish the linkage between daily OD trips and dynamic traffic assignment (DTA) procedures; thus results from transportation planning projects, in terms of daily OD trips, can be extended to estimate time-dependent OD trips. Field data from Taiwan are collected and applied in the calibration and validation processes. Dynamic Network Assignment-Simulation Model for Advanced Road Telematics (DYNASMART-P), a simulation-based DTA model, is applied to generate time-dependent flows. The results from the validation process show high agreement between actual flows from vehicle detectors (VDs) and simulated flows from DYNAMSART-P.     

Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Traffic flow prediction is an essential part of intelligent transportation systems (ITS). Most of the previous traffic flow prediction work treated traffic flow as a time series process only, ignoring the spatial relationship from the upstream flows or the correlation with other traffic attributes like speed and density. In this paper, we utilize a linear conditional Gaussian (LCG) Bayesian network (BN) model to consider both spatial and temporal dimensions of traffic as well as speed information for short‐term traffic flow prediction. The LCG BN allows both continuous and discrete variables, which enables the consideration of categorical variables in traffic flow prediction. A microscopic traffic simulation dataset is used to test the performance of the proposed model compared to other popular approaches under different predicting time intervals. In addition, the authors investigate the importance of spatial data and speed data in flow prediction by comparing models with different levels of information. The results indicate that the prediction accuracy will increase significantly when both spatial data and speed data are included. Copyright © 2016 John Wiley & Sons, Ltd.     

A planning methodology for intelligent urban transportation systems

Recent developments in intelligent transportation systems pose new challenges and opportunities for urban transportation planning. To meet these challenges and to exploit these opportunities, a framework for a new transportation planning methodology has been developed. The methodology operates in a computer environment, called PLANiTS (Planning and Analysis Integration for Intelligent Transportation Systems), designed to facilitate the entire planning process form problem identification, through idea generation and analysis, on to prioritization and programming. To assist in problem identification, PLANiTS provides graphic representation of current conditions, including traffic, air pollution, accidents, and projections of future conditions. A computerized knowledge base, containing information about possible strategies and their effects, and a model base, containing transportation and other analysis models, are used to guide the user in identifying potentially effective strategies and performing the appropriate analysis. To facilitate the use of these tools, PLANiTS provides computer support of group processes such as brainstorming, deliberation, and consensus seeking. PLANiTS is designed for use in urban transportation planning at the local, regional, and state levels; it is intended to support a variety of participants in the planning process including transportation professionals, decision makers in transportation agencies (often local elected officials), citizens, and interest groups. Recognizing that transportation planning is essentially a deliberative, political process, PLANiTS is designed to inform and facilitate, but not replace, the political decision-making process.     

A system dynamics approach to land use / transportation system performance modeling Part II: Application

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model system consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is also examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited to some extent due to data availability, the results clearly indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling     

Modelling and simulation of transportation system effectiveness to reduce traffic congestion: a system dynamics framework

ABSTRACT

This paper is designed to evaluate and improve the effectiveness of transportation systems and reduce traffic congestion through the use of simulation models and scenario development. A system dynamics framework is used to test and evaluate the alternatives of future strategies for the city of Surabaya, Indonesia. Some factors affecting the effectiveness of transport systems include operational effectiveness and service effectiveness, as well as uncertainty. To improve the effectiveness of transportation systems, several strategies can be implemented, such as subsidizing public transportation, increasing the cost of private vehicle parking fees, raising taxes on private vehicles, and reducing delays in public transportation through scenario development. Scenario results show that, by pursuing these strategies, effectiveness could be improved by 80% as the impact of the increase in operational and service effectiveness, helping to mitigate traffic congestion. Congestion could be reduced to 70% (on average) due to the decrease in daily traffic.     

公路交通噪声监测方法和防治措施研究

本文分析了交通噪声产生的危害,强调交通噪声防治的重要性,结合现行高速公路交通噪声声环境功能区质量监测、衰减断面监测等监测方法,从路线选择、新技术运用、声传递控制等方面综述了可行且有效的交通噪声防治方法,并提出应基于降低噪声污染源源强角度出发,加强降噪路面及低噪声运输工具研发工作,从源头控制噪声污染,从根本上解决噪声污染问题的建议。     

Analyzing the impact of a planned transit-oriented development on mode share and traffic conditions

ABSTRACT

Transit-oriented development (TOD) is a popular planning strategy used to maximize accessibility to transit for various trip purposes. The quantitative effects of TOD on travel mode shift and traffic congestion have not been extensively tested in the current literature. This paper utilizes a seemingly unrelated regressions (SUR) mode share model and a mesoscopic dynamic traffic assignment (DTA) model to analyze the impact of a planned TOD in Maryland. The proposed model aims at improving the understanding of the quantitative impacts of such a TOD on mode share and traffic congestion. The main result of the mode share model indicates that the increase in transit ridership for a transit accessible shopping center is not that significant. Local traffic conditions will deteriorate due to a lack of investment in road infrastructure planned for the TOD area. The proposed method could be a valuable tool for other indicative land development or transportation policy analyses.     

A system dynamics approach to land use/transportation system performance modeling Part I: Methodology

This paper presents a system dynamics approach to simultaneous land use/transportation system performance modeling. A model is designed based on the causality functions and feedback loop structure between a large number of physical, socioeconomic, and policy variables. The model consists of 7 sub‐models: population, migration of population, household, job growth‐employment‐land availability, housing development, travel demand, and traffic congestion level. The model is formulated in DYNAMO simulation language, and tested on a data set from Montgomery County, MD. In Part I: Methodology, the overall approach and the structure of the model system is discussed and the causal‐loop diagrams and major equations are presented. In Part II: Application, the model is calibrated and tested with data from Montgomery County, MD. Least square method and overall system behavior are used to estimate the model parameters. The model is fitted with the 1970–80 data and validated with the 1980–1990 data. Robustness and sensitivities with respect to input parameters such as birth rate or regional economy growth are analyzed. The model performance as a policy analysis tool is examined by predicting the year by year impacts of highway capacity expansion on land use and transportation system performance. While this is a first attempt in using dynamic system simulation modeling in simultaneous treatment of land use and transportation system interactions, and model development and application are limited due to data availability, the results indicate that the proposed method is a promising approach in dealing with complex urban land use/transportation modeling.     

VISSIM/MOVES integration to investigate the effect of major key parameters on CO2 emissions

This paper looks at CO₂ emissions on limited access highways in a microscopic and stochastic environment using an optimal design approach. Estimating vehicle emissions based on second-by-second vehicle operation allows the integration of a microscopic traffic simulation model with the latest US Environmental Protection Agency’s mobile source emissions model to improve accuracy. A factorial experiment on a test bed prototype of the I-4 urban limited access highway corridor located in Orlando, Florida was conducted to identify the optimal settings for CO₂ emissions reduction and to develop a microscopic transportation emission prediction model. An exponentially decaying function towards a limiting value expressed in the freeway capacity is found to correlate with CO₂ emission rates. Moreover, speeds between 55 and 60 mph show emission rate reduction effect while maintaining up to 90% of the freeway’s capacity. The results show that speed has a significant impact on CO₂ emissions when detailed and microscopic analysis of vehicle operations of acceleration and deceleration are considered.     

A flexible traffic stream model and its three representations of traffic flow

To connect microscopic driving behaviors with the macro-correspondence (i.e., the fundamental diagram), this study proposes a flexible traffic stream model, which is derived from a novel car-following model under steady-state conditions. Its four driving behavior-related parameters, i.e., reaction time, calmness parameter, speed- and spacing-related sensitivities, have an apparent effect in shaping the fundamental diagram. Its boundary conditions and homogenous case are also analyzed in detail and compared with other two models (i.e., Longitudinal Control Model and Intelligent Driver Model). Especially, these model formulations and properties under Lagrangian coordinates provide a new perspective to revisit the traffic flow and complement with those under Eulerian coordinate. One calibration methodology that incorporates the monkey algorithm with dynamic adaptation is employed to calibrate this model, based on real-field data from a wide range of locations. Results show that this model exhibits the well flexibility to fit these traffic data and performs better than other nine models. Finally, a concrete example of transportation application is designed, in which the impact of three critical parameters on vehicle trajectories and shock waves with three representations (i.e., respectively defined in x-t, n-t and x-n coordinates) is tested, and macro- and micro-solutions on shock waves well agree with each other. In summary, this traffic stream model with the advantages of flexibility and efficiency has the good potential in level of service analysis and transportation planning.     

A stochastic computational model for yellow time determination and its application

This paper proposes a stochastic model to determine the yellow time according to the occurring probability of Type‐I dilemma zone (P_DZ). Unlike the conventional methods generally based on the deterministic traffic flow theory, the proposed model fully accounts for the randomness of input variables such as approaching speed, deceleration rate, perception‐and‐reaction time, and distance to stop‐line at the yellow onset. A theoretical model is firstly established, and a computational program incorporating Monte Carlo Simulation is then developed to facilitate its general solution. These two alternative solution approaches to derive P_DZ and Y are proposed, depending upon whether D/V and (τ + V/2d) follow certain analytical distributions or not. In addition, field data at a typical high‐speed highway intersection are collected to validate the model. Based on the validated model, comprehensive sensitivity analysis is conducted to look into the entire picture of the relationship between P_DZ and the distributions as well as correlations of the input variables. To demonstrate the application of the proposed model, the required yellow times for various conditions are calculated based on the acceptable levels of P_DZ, and representative application tables for typical cases are finally provided. With the aid of the proposed methodology, traffic engineers are capable of designing yellow time in a more sophisticated manner. Copyright © 2014 John Wiley & Sons, Ltd.