Congested Corridor Research Articles

Examining Congestion Pricing Scheme Effectiveness Using the Travel Time Congestion Index

This study investigated the effectiveness of congestion pricing (CP) using the travel time congestion index (TTCI), a congestion assessment tool, in Hyderabad, India. Initially, a set of hypothetical mode choice scenarios under the CP scheme were designed to collect car users’ perceptions based on a stated preference (SP) experiment. Based on the SP survey data, discrete travel behavior models were developed to estimate the probable modal trade-off among cars, two-wheelers, and public buses under the generated CP scenarios. Using the existing traffic, geometric, and land-use data from the most congested corridors of the study city, TTCI values were estimated for the base and future conditions, followed by commuter volume estimation for the different conditions using vehicle occupancy factors. Further, the commuter volume derived from modal trade-off under CP scenarios was converted into traffic volume for the identified congested corridors. Finally, the TTCI values were re-estimated using the final traffic volume and compared across worst, best-worst (intermediate), and best case CP scenarios for the base and future years. An annual average growth of 5% in traffic volume was considered. The results show a significant improvement in TTCI values across all identified corridors under CP implementation, indicating its effectiveness toward congestion alleviation. Such demonstration of CP effectiveness could play a major role in making CP a successful travel demand management measure for cities burdened with congestion.

Analisis Keterlambatan Pelaksanaan Pekerjaan Jalur Kereta Api Lintas Makassar – Parepare

The train is a means of mass transportation which generally consists of a locomotive and a series of trains or carriages. The series of trains or carriages are relatively large in size so that they are able to load passengers and goods on a large scale. For the transportation of goods in large quantities, a series of more than 50 trains drawn and/or pushed with several locomotives can be used, such as the Babaranjang train in South Sumatra.The train is an efficient transportation for a high number of passengers so it is very suitable for mass transportation of urban trains in congested corridors, but is also used for medium-distance passenger transportation or for the transportation of goods in large quantities. Because of its nature as an effective mass transportation, several countries are trying to make maximum use of it as the main means of land transportation both within cities, between cities, and between countries

A meso-to-macro cross-resolution performance approach for connecting polynomial arrival queue model to volume-delay function with inflow demand-to-capacity ratio

• Cross-resolution modelling approach for understanding the dynamic relationship between demand and supply and the resulting congestion. • Describe oversaturated system dynamics with parsimonious macroscopic analytical formulations with consistent mesoscopic queue vehicular fluid model. • Unified integration of multi-scale models provides city planners with a valid analytical framework to analyze queue saturation evolution process. • Estimate key model parameters from real-world data sets on heavily congested corridors. Although the macroscopic volume-delay function (VDF) has been widely used in static traffic assignment for transportation planning, the planning community has long recognized its deficiencies as a static function in capturing traffic flow dynamics and queue evolution process. In the existing literature, many queueing-based and simulation-based dynamic traffic assignment (DTA) models involving various traffic flow parameters have been proposed to capture traffic system dynamics on different spatial scales; however, how to calibrate these DTA models could still be a challenging task in its own right, especially for real-world congested networks with complex traffic dynamics. By extending the fluid-based polynomial arrival queue (PAQ) model with quadratic inflow rates proposed by Newell (1982) and cubic inflow rates by Cheng et al. (2022), this paper attempts to propose a cross-resolution Queueing-based Volume-Delay Function (QVDF) to explicitly establish a coherent connection between (a) the macroscopic average travel delay performance function in a long-term planning horizon and (b) the mesoscopic dynamic queuing model during a single oversaturated period. By introducing two types of elasticity functional forms, this paper develops a relationship from the macroscopic inflow demand-to-capacity (D/C) ratio to the congestion duration of a bottleneck, from the congestion duration to the magnitude of speed reduction. The QVDF can be directly utilized to provide closed-form expressions for both average travel delay performance and the time-dependent speed profiles. The proposed cross-resolution QVDF provides a numerically reliable and theoretically rigorous performance function to characterize oversaturated bottlenecks at both macroscopic and mesoscopic scales.

Congestion and environmental impacts of short car trip replacement with micromobility modes

Transportation is a basic social need, but most trips are done by private vehicles, which is not environmentally sustainable with growing urban populations. Micromobility (e.g., shared bikes) represents a significant opportunity to replace short private vehicles trips (0–3 miles) and reduce transportation sector emissions. This paper uses Seattle as a case study and estimates that up to 18% of short car trips could be replaced by micromobility. A static traffic assignment model is developed to simulate and compare the results of peak hour traffic under a base case scenario (2014 traffic conditions) to scenarios where a portion of short car trips are substituted by micromobility. Results indicate that micromobility could reduce congestion on heavily congested corridors and wide-scale bike lane deployment can maximize congestion benefits, but the impacts to energy use and emissions are disproportionately low and other measures (e.g., vehicle electrification) are needed to meet climate change emissions targets.

Effects of capacity and transit improvements on traffic and transit operations

ABSTRACT Transportation agencies are facing problems of inadequate capacities to meet growing traffic demand. Street widening, signal operations optimization, innovative designs and multimodal transportation modes are traffic management strategies that can improve traffic operations along congested corridors. The goal of this paper is to assess future 2040 traffic and transit conditions along sections of Redwood Road in Salt Lake County, Utah, and develop and test potential operational improvements through VISSIM traffic simulation. Potential improvements, such as street widening, signal optimization, transit exclusive lanes with Transit Signal Priority (TSP) and adding a Continuous Flow Intersection (CFI) are analyzed. The improvements tested showed potential for reducing vehicular travel times between 6% to 20%, and delays 25% to 35%. Transit travel times reduced up to 40%, with delay reductions up to 60%. A combination of different strategies has the potential to improve traffic and transit operations along the busiest sections of Redwood Road.

Addressing travel time and pedestrian safety issues along a congested corridor using microsimulation tool: A case Study of Bangalore, India.

Addressing travel time and pedestrian safety issues along a congested corridor using microsimulation tool: A case Study of Bangalore, India.

Integrating mesoscopic dynamic traffic assignment with agent-based travel behavior models for cumulative land development impact analysis

A number of approaches have been developed to evaluate the impact of land development on transportation infrastructure. While traditional approaches are either limited to static modeling of traffic performance or lack a strong travel behavior foundation, today’s advanced computational technology makes it feasible to model an individual traveler’s response to land development. This study integrates dynamic traffic assignment (DTA) with a positive agent-based microsimulation travel behavior model for cumulative land development impact studies. The integrated model not only enhances the behavioral implementation of DTA, but also captures traffic dynamics. It provides an advanced yet practical approach to understanding the impact of a single or series of land development projects on an individual driver’s behavior, as well as the aggregated impacts on the demand pattern and time-dependent traffic conditions. A simulation-based optimization (SBO) approach is proposed for the calibration of the modeling system. The SBO calibration approach enhances the transferability of this integrated model to other study areas. Using a case study that focuses on the cumulative land development impact along a congested corridor in Maryland, various regional and local travel behavior changes are discussed to show the capability of this tool for behavior side estimations and the corresponding traffic impacts.

A Methodology for Measuring and Monitoring Congested Corridors: Applications in Manhattan Using Taxi GPS Data

ABSTRACTThis paper describes how the Taxi Passenger Enhancement Program (TPEP) Trip Record data can be used to measure traffic congestion along selected corridors in Manhattan. Two corridors in the rapidly changing Far West Side, Ninth Avenue and Tenth Avenue, were selected for analysis in this case study. This paper will describe a methodology using GIS applications that can visualize and identify trends in traffic conditions over time along corridors selected for analysis. This methodology can be applied to evaluate and monitor congested corridors in response to changes in land use patterns, policy decisions, and various temporary conditions affecting street operations.

Operational performance of a congested corridor with lanes dedicated to autonomous vehicle traffic

Abstract This paper considers the operational performance impact of autonomous vehicles (AV) on a multi-lane freeway corridor with separate lanes dedicated to AV and non-AV traffic. Autonomous vehicle behavior is modeled at the macroscopic level by modifying the fundamental diagram relating hourly traffic flow and vehicle density, a step that is justified by adjusting a parameter from Newell’s car-following model at the microscopic level and transforming back to a macroscopic representation. The model is applied to the I-95 corridor between Washington, DC and Baltimore, MD during the PM peak period, where the impact of introducing a managed AV-only lane is assessed at varying penetration rates of autonomous vehicles. The results show that the overall corridor performance metrics improve with increasing penetration rates up to 30%, 40% or 50% (depending on the underlying assumptions that govern AV behavior), after which the performance deteriorates drastically. Implications of the results are discussed in light of the per-lane and aggregated metrics, and future directions for research are proposed.

Control Strategy for Vehicular and Pedestrian Midblock Crossing Movements

Abstract On major corridors where mid-block pedestrian crossing exists, traffic flows are frequently interrupted by pedestrian crossing movements. The pedestrian crossings operated by manual control may cause vehicular movements experience unexpected stops and excess travel delay. Specifically in developing countries, the signal timing and pedestrian crossing operations are intuitively judged by individual police patrols or crossing guards. The objectives of this study are (i) to examine the decision mechanism of the crossing guards; (ii) to develop the simulation models that replicate the crossing guard control at midblock pedestrian crossings and evaluate their operational performances; and (iii) to determine the most reasonable and viable control strategy that balances the mobility of vehicular and pedestrian flows. This study proposes a fuzzy logic control strategy to analyse the optimal traffic signal setting for pedestrian crossing signals along an urban corridor. The fuzzy logic signal control is used to characterize subjective and imprecise decision made by police patrols. It is applicable to deal with a complex traffic condition. The study tests the proposed scheme with the real-world congested corridor in Chiang Mai, Thailand. Traffic data were collected from the field during the peak period; and the opinions of the judgment rules were gathered from the police patrols. The study used a microscopic traffic simulation to evaluate the measures of effectiveness of the proposed control strategy and compare its performance with the conventional pedestrian crossing signal including fixed-time and demand-actuated signal operation. The expected results of this study are (i) an alternative approach to control traffic signals for both vehicular and pedestrian flows; and (ii) a simulation-based environment that can evaluate vehicular and pedestrian performance measures.

Evaluation of Operational Improvement Strategies to Reduce Congestion on Urban Corridors

In recent years, traffic congestion has become a major problem in transport networks particularly in peak periods. The congestion problem has become worse in large cities, particularly in the CBDs over the last few decades and the problem intensifies every year. Traffic congestion results in longer travel times, larger delays, more fuel consumptions and more emission. Australian Bureau of infrastructure, transport and regional economics estimates that the avoidable social costs of congestion will be more than doubled in 2020. This research intends to propose a solution to reduce traffic congestion and consequently traffic delay for a congested corridor in Melbourne metropolitan area. To achieve this, a number of strategies are introduced and their influence in traffic congestion is evaluated. Dedicated bus /tram lane, Bus/tram stop location change, Parking restriction, Bicycle lane restriction, Passenger car movement restriction, Lane configuration Advanced Green Signal and Extended Green signal will be applied to reduce congestion strategies. The site selected for this research is a corridor from inner suburbs of Melbourne.

Using Regional Archived Multimodal Transportation System Data for Policy Analysis

Development of a comprehensive historical archive of regional real-time multimodal multiagency transportation system data makes possible more detailed study of impacts of major transport investments on travel and system performance. We illustrate with a case study of a new light rail transit line. We use transit, freeway, and arterial data of high spatial and temporal resolution to examine transportation system performance impacts of the Exposition (Expo) light rail line (Phase 1) in Los Angeles. Using a quasi-experimental research design, we explore the impact of the Expo Line on transit ridership, freeway traffic, and arterial traffic within the corridor it serves. Our results suggest a net increase in transit ridership, but few effects on roadway traffic system performance. Given the latent travel demand in this heavily congested corridor, results are consistent with expectations. The benefits of rail transit investments are in increasing transit accessibility and person throughput within high-demand corridors; effects on roadway traffic are small and localized.

Value of Dynamic Revenue-Maximizing Congestion Pricing in a Highly Congested Corridor

AbstractThis paper examines the value of utilizing dynamic, revenue-maximizing, congestion pricing on a privately operated tolled route, when the only alternative is a highly congested, public, free-access route. Three methods are specified by which revenue can be maximized—through the selection of a fixed percent of users to set the toll price for, through the selection of a fixed level of service on the toll road, and through the use of a nonlinear optimization model. These methods are tested on a case study involving a highly congested corridor. In simulation, the corridor’s flow rates are observed following the posting of a toll, the toll is updated, and the new flow observed until a subsequent round of revenue maximizing is applied to the new conditions. It was concluded that in a highly congested corridor a dynamic, revenue-maximizing toll can finance the construction of new capacity without degrading social welfare.

Design of Real-Time Emergency Response System for Highway Networks

This paper proposes a general framework of real-time emergency response operations for highway networks experiencing a high frequency of concurrent traffic emergency events. The proposed system consists of three principal models working collectively to optimize the location assignment of available response units, to estimate the probabilities of event occurrences, and to project the incident clearance time. This system was designed to assist responsible agencies in assessing the need to relocate available incident response units in real-time operations on the basis of the available resources and detected traffic information. Because the proposed system was designed mainly for the traffic incident management teams to respond to the traffic emergency events, it aimed to improve the networkwide traffic condition and the service performance by solving multiple conflicting objectives. This aim is different from most existing studies that target mainly emergency medical service. The empirical evaluation results using the incident data from I-695–MD-695 in Maryland showed that the dynamic real-time dispatch strategy could outperform the static dispatch and state-of-the-practice patrolling strategies with respect to minimizing the total delays induced by incidents. Furthermore, the dynamic dispatch strategy will enable the operating agency to use the available resources better than will the static dispatch strategy, especially when many traffic emergency events in congested corridors may occur in a relatively short time period.

Accounting for travel time variability in the optimal pricing of cars and buses

A number of studies have shown that in addition to travel time and cost as the common influences on mode, route and departure time choices, travel time variability plays an increasingly important role, especially in the presence of traffic congestion on roads and crowding on public transport. The dominant focus of modelling and implementation of optimal pricing that incorporates trip time variability has been in the context of road pricing for cars. The main objective of this paper is to introduce a non-trivial extension to the existing literature on optimal pricing in a multimodal setting, building in the role of travel time variability as a source of disutility for car and bus users. We estimate the effect of variability in travel time and bus headway on optimal prices (i.e., tolls for cars and fares for buses) and optimal bus capacity (i.e., frequencies and size) accounting for crowding on buses, under a social welfare maximisation framework. Travel time variability is included by adopting the well-known mean–variance model, using an empirical relationship between the mean and standard deviation of travel times. We illustrate our model with an application to a highly congested corridor with cars, buses and walking as travel alternatives in Sydney, Australia. There are three main findings that have immediate policy implications: (i) including travel time variability results in higher optimal car tolls and substantial increases in toll revenue, while optimal bus fares remain almost unchanged; (ii) when bus headways are variable, the inclusion of travel time variability as a source of disutility for users yields higher optimal bus frequencies; and (iii) including both travel time variability and crowding discomfort leads to higher optimal bus sizes.

A Dynamic Offset Model based on Stop Line Detector Information

Existing traffic networks have to be controlled and regulated efficiently before trying to build new transportation facilities, with an aim to reduce the growing traffic congestion. One of the effective option is signal coordination which helps in improving the uninterrupted flow of traffic. The offset provided for traffic signal coordination is often static assuming constant link travel time making it unfit for varying traffic flows. Present study is aimed at building a dynamic offset model, based on the flow rate and the link length between the intersections meant for coordination. Estimation of travel time required for the vehicles to travel from one intersection to another forms the basis of the offset calculation. The underlying assumption is that the travel time increases with an increase in the rate of vehicular flow, as well as the distance between the intersections. This can be captured by a vehicle detector typically placed at stop line. The relation between travel time and vehicular flow has been verified by conducting studies in congested corridors in Mumbai, India, during peak and off peak hours. An algorithm similar to a vehicle actuated algorithm, but terminate at some common cycle length is used in the development of the models and evaluated using a robust traffic simulator. A non-linear dynamic offset model is hence built, which caters to the real time fluctuating traffic conditions. However, the estimation of vehicular flow rate from the detectors in the field is sometimes difficult and erroneous. To address this limitation, a second model is proposed based on the assumption that the green time provided by the algorithm can be used as a proxy for the detector output and consequently the level of congestion. Accordingly, a dynamic offset model is developed which gives the value of the offset to be implemented based on the green time used and the link length. The models developed are compared with the independent vehicle actuated control and constant offset control. The average delay and travel time are considered as the performance indicator. It is observed that the dynamic offset is successful in improving the coordination efficiency of corridors, even for heterogeneous traffic conditions.

Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance

The Sydney Coordinated Adaptive Traffic System (SCATS) is designed to improve mobility on congested corridors with variable demand; SCATS is expected to improve transit performance. Because many urban corridors are heavily used by transit routes, it is important to determine how SCATS affects transit performance on congested corridors. However, there is limited research on evaluating the impact of SCATS on transit performance. To the best of the authors’ knowledge, this research presents the first field evaluation of SCATS with transit signal priority in the United States. The case study is based on before-and-after traffic and transit data along Powell Boulevard, one of the most congested urban arterials in the Portland, Oregon, metropolitan region. Using permanent traffic data collection stations, bus automated vehicle location and automatic passenger count data, and transit signal priority request data, this work presents the results of statistical tests and regressions to determine the impacts of SCATS. Statistically significant differences were observed in travel times and SCATS-related regression parameters. Overall, travel time changes or improvements related to SCATS seemed to depend greatly on the direction of travel and time of day.

Statistical Study of the Impact of Adaptive Traffic Signal Control on Traffic and Transit Performance

The Sydney Coordinated Adaptive Traffic System (SCATS) is designed to improve mobility on congested corridors with variable demand; SCATS is expected to improve transit performance. Because many urban corridors are heavily used by transit routes, it is important to determine how SCATS affects transit performance on congested corridors. However, there is limited research on evaluating the impact of SCATS on transit performance. To the best of the authors’ knowledge, this research presents the first field evaluation of SCATS with transit signal priority in the United States. The case study is based on before-and-after traffic and transit data along Powell Boulevard, one of the most congested urban arterials in the Portland, Oregon, metropolitan region. Using permanent traffic data collection stations, bus automated vehicle location and automatic passenger count data, and transit signal priority request data, this work presents the results of statistical tests and regressions to determine the impacts of SCATS. Statistically significant differences were observed in travel times and SCATS-related regression parameters. Overall, travel time changes or improvements related to SCATS seemed to depend greatly on the direction of travel and time of day.

Impact of Traffic Signal Timing on Sidewalk-Level Particulate Matter Concentrations

Improving the efficiency of urban traffic operations along arterials is a priority for many agencies because congestion affects the movement of people and goods in many cities. Advanced traffic management systems are being implemented to optimize traffic signal timing in congested corridors. Pedestrians and transit users are even more exposed to vehicle emissions than are drivers. However, pedestrian exposure to traffic emissions is typically not a consideration when traffic signal timing decisions are made. The relationship between exposure to air pollution and traffic signal timing has not yet been fully explored or modeled. This paper quantifies the factors that contribute to concentrations of sidewalk-level particulate matter (1.0 to 2.5 μm in diameter) at a busy intersection along an urban arterial in Portland, Oregon. The study is the first research effort to combine real-world, detailed traffic signal timing data (at 5-s intervals) and air pollutant concentration data. Several types of variables are included in the statistical analysis: traffic signal timing variables, weather-related variables, traffic volume and composition variables, and variables associated with bus presence and characteristics. Statistical results show the importance of signal timing variables, traffic volumes, and queuing.

PASSENGER PREFERENCE ANALYSIS: LIGHT RAIL TRANSIT OR BUS VERSUS CAR

To begin with, his article studies the user's preferences when faced with the introduction of two completely different public transport systems: the bus and the Light Rail Transit (LRT). Also, it examines how the modal distribution varies between the private car and each of the new systems, in particular. In addition, the most important variables for users when travelling in a congested corridor are individualized and a study is made on how these variables influence on the modal distribution. The results of the stated preferences of surveys are modelled using mixed logit models. Values are estimated for user's time and the demand elasticity is determined with respect to the relevant variables. Different situations are created to analyse how user's behaviour changes with the changes in the most important variables. Furthermore, this article quantifies the effects of the more relevant variables and shows that the LRT can attract more demand than a bus service, but it must guarantee a regular and frequent service, at the same time as charging competitive fares. It is also shown that for the introduction to be more successful any taken action should be accompanied by policies that chastise the use of the private car such as increased parking charges or higher fuel taxes.