Traffic Demand Levels Research Articles

Designing Sustainable Public Transportation: Integrated Optimization of Bus Speed and Holding Time in a Connected Vehicle Environment

Developing public transportation and giving priority to buses is a feasible solution for improving the level of public transportation service, which facilitates congestion alleviation and prevention, and contributes to urban development and city sustainability. This paper presents a novel bus operation control strategy including both holding control and speed control to improve the level of service of transit systems within a connected vehicle environment. Most previous work focuses on optimization of signal timing to decrease the bus signal delay by assuming that holding control is not applied; the speed of buses is given as a constant input and the acceleration and deceleration processes of buses can be neglected. This paper explores the benefits of a bus operation control strategy to minimize the total cost, which includes bus signal delay, bus holding delay, bus travel delay, acceleration cost due to frequent stops and intense driving. A set of formulations are developed to explicitly capture the interaction between bus holding control and speed control. Experimental analysisand simulation tests have shown that the proposed integrated operational model outperforms the traditional control, speed control only, or holding control only strategies in terms of reducing the total cost of buses. The sensitivity analysis has further demonstrated the potential effectiveness of the proposed approach to be applied in a real-time bus operation control system under different levels of traffic demand, bus stop locations, and speed limits.

Network topological effects on the macroscopic Bureau of Public Roads function

Cost flow functions are a central class of models in the transport field because they are an essential ingredient of static user equilibrium traffic assignment and transport policy and planning analysis. Macroscopic cost flow (MCF) functions, which model travel cost at different levels of network use, have gained much attention in recent decades due to their potential applications in area-wide traffic management and control, and initial land-use planning. They are the collective outcomes of the responses of road users to the existing transportation network and the interactions between road users at different levels of traffic demand. Network topological effects have long been anticipated to be the primary factors governing the shape and performance of a network. However, few studies have investigated network topology. This paper aims to unveil the direct link between network topological metrics and the parameters of a specific MCF, in the form of macroscopic Bureau of Public Roads (MBPR) function, with the support of real-world data. Seventy-one 1 km × 1 km urban built-up regions were sampled in Hong Kong. The MBPR functions of the selected regions were calibrated using taxi global positioning system data. Intensive investigations revealed that the average number of junctions per unit distance and the road density were topological features correlating with the free-flow travel time and congestion sensitivity parameters of the MBPR function, respectively. A spatially variable MBPR function was established.

Enhanced Decision-Making Framework Using Reliability Concepts for Freeway Facilities

This paper presents a decision-making framework based on a travel time reliability methodology developed under the U.S. Strategic Highway Research Program. Existing methods consider a set of predefined prevailing conditions for the analysis of freeway facilities as the base case. However, a reliability analysis accounts for multiple recurring and nonrecurring congestion sources to estimate the travel time distribution over a long time horizon. This approach considers variations in traffic demand levels, inclement weather conditions, and incidents that occur stochastically on a freeway facility. Several performance measures are defined based on the travel time distribution, which comprehensively cover the full range of operational conditions on the system. Based on the proposed decision-making framework, mobility strategies can be identified, evaluated, and improved.

Multimodal Intelligent Traffic Signal System Simulation Model Development and Assessment

The study demonstrated the multimodal intelligent traffic signal system (MMITSS) simulation tool to assess the potential effects of a broader MMITSS deployment that would ultimately facilitate the site-independent analysis of MMITSS applications. The study also identified the most beneficial operational conditions for each MMITSS operational scenario by considering a combination of simulation variables and traffic demand levels. The MMITSS simulation platform consists of the Vissim microscopic traffic simulation software, the basic safety message distributor program, an Econolite ASC/3 traffic controller emulator that runs on a Windows platform, and a roadside equipment module that runs on a Linux platform. The study demonstrated that the intelligent traffic signal system reduced vehicle delay by up to 35% and increased average traffic stream speed by up to 27%. In addition, transit signal priority (TSP) reduced travel time for both transit and passenger vehicles on the corridor by up to 29% and 28%, respectively. However, TSP can increase systemwide delay because it reduces green times on the side streets. Freight signal priority (FSP) can be used effectively along major freight routes. Although FSP significantly reduced truck delay, networkwide delay was increased substantially, especially in the scenario with a large percentage of trucks in the traffic composition. The TSP and FSP bundle simulation study demonstrated that the bundle operation successfully executed a hierarchical level of priority and provided higher priority for trucks. MMITSS applications effectively improve vehicle travel time and delay for equipped and potentially nonequipped vehicles, depending on the scenarios considered. Typically, systemwide disbenefits are observed when TSP, FSP, or both are applied.

Modeling Evaluation of Eco–Cooperative Adaptive Cruise Control in Vicinity of Signalized Intersections

Vehicle stops caused by traffic signals reduce vehicle fuel economy ratings along arterial roadways. Eco–cooperative adaptive cruise control (eco-CACC) systems are being developed in an attempt to improve vehicle fuel efficiency in the vicinity of signalized intersections. These eco-CACC systems utilize traffic signal phasing and timing data received through vehicle-to-infrastructure communication, together with vehicle queue predictions, to compute fuel-optimum vehicle trajectories that are continuously updated as the vehicle travels in the vicinity of signalized intersections. The algorithm computes a desired speed for the vehicle that is either displayed to the driver or directly integrated into the vehicle’s adaptive cruise control system. In this paper, the INTEGRATION microscopic traffic assignment and simulation software is used to evaluate the performance of a proposed eco-CACC algorithm to assess its networkwide energy and environmental impacts. A simulation sensitivity analysis demonstrates that as the market penetration rate of CACC-equipped vehicles increases, the energy and environmental benefits also increase, and that the overall savings in fuel consumption are as high as 19% when the market penetration rate is 100%. On multilane roads, the algorithm may produce networkwide increases in the fuel consumption level when the market penetration rate is less than 30%. The analysis also demonstrates that the length of control segments, the signal phasing and timing plan, and the traffic demand levels significantly affect the algorithm performance. The study further demonstrates that the algorithm may produce increases in fuel consumption levels when the network is oversaturated; thus, further work is needed to enhance the algorithm for these conditions.

Fuzzy-logic framework for future dynamic cellular systems

There is a growing need to develop more robust and energy-efficient network architectures to cope with ever increasing traffic and energy demands. The aim is also to achieve energy-efficient adaptive cellular system architecture capable of delivering a high quality of service (QoS) whilst optimising energy consumption. To gain significant energy savings, new dynamic architectures will allow future systems to achieve energy saving whilst maintaining QoS at different levels of traffic demand. We consider a heterogeneous cellular system where the elements of it can adapt and change their architecture depending on the network demand. We demonstrate substantial savings of energy, especially in low-traffic periods where most mobile systems are over engineered. Energy savings are also achieved in high-traffic periods by capitalising on traffic variations in the spatial domain. We adopt a fuzzy-logic algorithm for the multi-objective decisions we face in the system, where it provides stability and the ability to handle imprecise data.

A network level connectivity robustness measure for connected vehicle environments

This study introduces a new CONnectivity ROBustness model (CONROB) to assess vehicle-to-vehicle communication in connected vehicle (CV) environments. CONROB is based on Newton’s universal law of gravitation and accounts for multiple factors affecting the connectivity in CV environments such as market penetration, wireless transmission range, spatial distribution of vehicles relative to each other, the spatial propagation of the wireless signal, and traffic density. The proposed methodology for the connectivity robustness calculation in CONROB accounts for the Link Expiration Time (LET) and the Route Expiration Time (RET) that are reflected in the stability of links between each two adjacent vehicles and the expiration time of communication routes between vehicles. Using a 117sq-km (45-square mile) network in Washington County, located west of Portland city, Oregon, a microscopic simulation model (VISSIM) was built to verify CONROB model. A total of 45 scenarios were simulated for different traffic densities generated from five different traffic demand levels, three levels of market penetration (5%, 15%, and 25%), and three transmission range values [76 (250), 152 (500), and 305 (1000)m (ft)]. The simulation results show that the overall robustness increases as the market penetration increases, given the same transmission range, and relative traffic density. Similarly, the overall connectivity robustness increases as the relative traffic density increases for the same market penetration. More so, the connectivity robustness becomes more sensitive to the relative traffic density at higher values of transmission range and market penetration. Multiple regression analysis was conducted to show the significant effect of relative traffic density, transmission range, and market penetration on the robustness measure. The results of the study provide an evidence of the ability of the model to capture the effect of the different factors on the connectivity between vehicles, which provides a viable tool for assessing CV environments.

Joint Analysis of Queuing Delays Associated With Secondary Incidents

Incidents cause substantial travel delays on urban freeways. Travel delays due to incidents are typically estimated for each incident independently. However, when multiple incidents occur on the same stretch of a roadway, treating them independently and ignoring their interactions leads to inaccurate results, which ultimately may lead to inefficient incident management strategies. This study examines delays due to primary–secondary incident pairs that occur on the same stretch of a freeway within a short time gap. Depending on their correlation in time and space, they can have varying degrees of impacts on the traffic flow. This article assesses the total delays induced by primary–secondary incident pairs by jointly modeling their occurrences. First, incident data combined with roadway inventory data from Hampton Roads, Virginia, are analyzed to understand the attributes of primary–secondary incident pairs, e.g., durations, lane blockages, and time gaps (between start times of the primary and its secondary incidents). Then using microscopic simulation as a primary evaluation tool, the effects of three critical parameters are investigated: time gap, distance between primary and secondary incidents, and traffic demand level. The historical incident analysis shows that, on average, primary–secondary incident pairs have substantially longer durations than single incidents. The joint analysis of queuing delays induced by primary incident and its secondary incident suggests that time gap and distance between a primary incident and its secondary incident are significantly associated with the total delays after controlling for other factors (e.g., the two associated incidents’ durations and lane blockages). Furthermore, based on the scenarios tested (i.e., two incidents both blocking the right lane in a three-lane basic freeway section), it is found that the sum of delays induced by a primary incident and its secondary incident separately will over- or underestimate the actual delays. For those secondary incidents that end after their associated primary incidents, total delays increase as time gap increases; increasing the distance between primary and secondary incidents is associated with reduction in delays. Other results and the implications of the findings for traffic operations are presented in the article.

Air quality impact of intelligent transportation system actions used in a decision support system for adaptive traffic management

The presented traffic control system (CARBOTRAF) combines real-time monitoring of traffic and air pollution with simulation models for traffic, emission and local air quality predictions to deliver on-line recommendations for alternative adaptive traffic management. The aim of introducing a CARBOTRAF system is to reduce BC and CO2 emissions and improve air quality by optimising the traffic flows. A chain of models combines microscopic traffic simulations, emission models and air quality simulations for a range of traffic demand levels and intelligent transport system (ITS) actions. These ITS scenarios simulate combinations of traffic signal optimisation plans and variable messaging systems. The real-time decision support system uses these simulations to select the best traffic management in terms of traffic and air quality. In this paper the modelled effects of ITS measures on air quality are analysed with a focus on BC for urban areas in two European cities, Graz and Glasgow.

A Fully-Distributed Heuristic Algorithm for Control of Autonomous Vehicle Movements at Isolated Intersections

Optimizing autonomous vehicle movements through roadway intersections is a challenging problem. It has been demonstrated in the literature that traditional traffic control, such as traffic signal and stop sign control are not optimal especially for heavy traffic demand levels. Alternatively, centralized autonomous vehicle control strategies are costly and not scalable given that the ability of a central controller to track and schedule the movement of hundreds of vehicles in real-time is questionable. Consequently, in this paper a fully distributed algorithm is proposed where vehicles in the vicinity of an intersection continuously cooperate with each other to develop a schedule that allows them to safely proceed through the intersection while incurring minimum delay. Unlike other distributed approaches described in the literature, the wireless communication constraints are considered in the design of the control algorithm. Specifically, the proposed algorithm requires vehicles heading to an intersection to communicate only with neighboring vehicles, while the lead vehicles on each approach lane share information to develop a complete intersection utilization schedule. The scheduling rotates between vehicles to identify higher traffic volumes and favor vehicles coming from heavier lanes to minimize the overall intersection delay. The simulated experiments show significant reductions in the average delay using the proposed approach compared to other methods reported in the literature and reduction in the maximum delay experienced by a vehicle especially in cases of heavy traffic demand levels.

Estimating Approach Path Coverage of Aircraft-Derived Meteorological Data in Advanced Air Traffic Management Applications

If appropriately equipped, aircraft traveling throughout the global air space have the unique capability to measure and report high-resolution meteorological data, under all weather conditions. These data can have high economic value for both aviation and nonaviation uses by communicating a dense picture of weather conditions from thousands of ad hoc sensors operating daily in the global air space over areas of an aviation operational interest. However, ensuring that the technology provides adequate levels of service and safety requires a clear understanding of system performance characteristics in conjunction with sensor and data link capabilities, broadcast frequencies, equipage rates, and operational service environment requirements. This paper explores the density of coverage as a measure of performance by presenting the problem as a stochastic area coverage model of a homogeneous dynamic wireless ad hoc sensor network. A model was developed to relate the density needs of various advanced air traffic management applications to the spatial and temporal coverage of aircraft-sensed meteorological data in the airport terminal area. A comprehensive experimental design is presented with varying aircraft equipage rates, message broadcast frequencies, coverage granularities, and traffic demand levels. Infrastructure and sensor limitations are included to capture some realism within the model. Results are determined by solving this variation on a traditional set covering model using Monte Carlo simulation techniques.

Characterize Dilemma Zone and Minimize its Effect at Coordinated Signalized Intersections

Dilemma zone at signalized intersection has been recognized as a major potential factor causing rear-end and right-angle crashes. However, the impacts of dilemma zone have seldom been considered in optimization procedures of signal coordination. This paper conducts a comprehensive evaluation of effects of optimization objectives for signal coordination at two coordinated signalized intersections. Microscopic traffic simulation software, VISSIM, was used to represent the stochastic variation of traffic flow. The results indicate that the traffic flow fluctuation, the optimization objectives, and traffic demand level have significant impacts on dilemma zone distribution. Compared with length of dilemma zone, the number of vehicles in dilemma zone is more sensitive to the signal coordination. The method to avoid dilemma zone is also proposed based on simulation results.

An adaptive control algorithm for traffic-actuated signals

A real-time, on-line control algorithm is proposed that aims to maintain the adaptive functionality of actuated controllers while improving the performance of traffic-actuated signal control system. To be consistent with the operation logic of existing signal control devices, only those four basic control parameters that can be found in modern actuated controllers are considered: phase sequence, minimum green, unit extension and maximum green. Microscopic simulation is used to test and evaluate the proposed control algorithm comparing with free-mode actuated, actuated-coordinated and volume–density control in a calibrated signalized network. Simulation results indicate that the proposed algorithm has the potential to improve the performance of the network at different traffic demand levels.

A rule‐based model for integrated operation of bus priority signal timings and traveling speed

SUMMARYThis paper focuses on integrated operation of signal timings and bus speed to provide priority to buses at isolated intersections when real‐time adjustment of bus speed is available (e.g., through Connected Vehicle). Most previous work assumes that the speed of a bus is given as an exogenous input and focuses merely on optimization of signal timings. The bus‐passing window and the bus‐arriving window are defined with respect to the real‐time signal status and bus arrivals to capture explicitly the interaction between bus speed and transit priority signal timings. A set of integrated operational rules is developed on the basis of these windows for buses with and without schedule deviation with the objective of minimizing bus schedule deviation, bus fuel consumption, and emissions. Four subsets are included: impacts of preceding bus analysis rules, priority requests generation rules, priority passing rules, and speed adjustment without priority rules. A VISSIM‐based simulation platform was designed and used for simulating and evaluating the proposed method. Extensive experimental analyses have shown that the proposed rule‐based integrated operational approach outperforms the no priority and conventional priority strategies (no bus speed adjustment) in terms of reducing bus delays, improving schedule adherence, saving energy, reducing emission, and minimizing the impacts on general traffic. The sensitivity analysis has further demonstrated the potential of the proposed approach to be applied in real‐time bus priority control system under different levels of transit and traffic demand. Copyright © 2012 John Wiley & Sons, Ltd.

A Dynamic Programming Approach for Optimal Signal Priority Control Upon Multiple High-Frequency Bus Requests

This article presents a priority signal control model for multiple bus requests. The proposed model aims to generate the optimal priority serving sequence to maximize the utilization of available green times by buses, but not to incur excessive congestion for other vehicular traffic. This study first depicts the serving sequence for multiple priority requests as a multistage decision process and explicitly models three bus priority strategies under the constraints of minimum green time, acceptable degree of saturation, and length of priority window. Further, it formulates a dynamic programming model to optimize the serving sequence for multiple priority requests as well as the corresponding signal timing plans under various levels of bus occupancy, schedule deviation, and traffic demand. A rolling time horizon approach is employed to solve the proposed model in real time. Comparative analysis results have shown that the proposed dynamic programming model outperforms the first-come-first-serve policy in terms of reducing bus delays, improving schedule adherence, and minimizing the impacts on other vehicular traffic. Computational performance analysis has further demonstrated the potential of the proposed model and algorithm to be applied in real-time bus priority control system.

Sensitivity of a real-time freeway crash prediction model to calibration optimality

BackgroundReal-time crash prediction models are often structured as general log-linear categorical models which must be calibrated using an extensive database. However, there is no method to optimally select the number of categories and the values that define the boundaries between categories when representing continuous measures as categorical variables within the log-linear model. This raises the question of how important the calibration is to the safety impacts estimated when using the crash prediction model. In this paper, we examined the impact that the process used to calibrate the crash prediction model has on estimates of safety impacts of a variable speed limit system.MethodsTwo calibration methods were compared, namely a heuristic ad hoc method and a nearoptimal method. Both methods were applied to calibrate a crash prediction model using the same set of data from an urban freeway in Ontario, Canada. The calibrated crash prediction models are used to evaluate the safety benefits of a candidate variable speed limit system under three different traffic demand levels (Peak, Near-Peak, and Off-Peak).Concluding remarksIt was found that safety improvements estimated by the two calibrated crash prediction models are within approximately 13% of each other for the Peak and Near-Peak scenarios, but differ by a larger amount for the Off-Peak scenario. However, despite these differences in the estimated magnitude of the safety impacts, the sign of the impact (i.e. increase versus decrease in safety) were consistent irrespective of the calibration method used. The results suggested that the safety impacts provided by the crash prediction model are robust in that they are relatively insensitive to the optimality of the calibration.

Traffic Mobility Impact of Mileage-Based User Fees on Traveler Route Choice Behavior and Network Performance: Planning-Level Traffic Equilibrium-Based Approach

User fees based on vehicle miles traveled (VMT) are receiving much attention in policy debates as a means of financing the road infrastructure by eventually replacing the current funding model that uses fuel user fees. By focusing on partially modeling driver response to VMT user fees, this paper aims to investigate the potential traffic mobility effects of such fees from a traffic operational point of view under user equilibrium conditions. On the basis of a nonlinear optimization-based conceptual framework, the proposed traffic assignment model is evaluated by assuming heterogeneous values of time. A simple corridor with a single origin–destination pair is first used to demonstrate the sensitivity of network performance to the VMT user fees in a deterministic capacity environment. The effects of VMT user fees depend significantly on the characteristics of the underlying transportation network, such as the relative length of the alternative paths, the overall traffic demand level, and of course the mileage-based fee itself. An open-source mesoscopic simulator is used to assess the systemwide travel time effects of VMT user fees on a real-world network.

A Cooperative Distributed System for Real-Time Route Guidance

This paper describes a cooperative decentralized architecture for reactive real-time route guidance. The architecture is cooperative in the sense that it allows adjacent local controllers to exchange information regarding the traffic conditions in their territories. A set of local decision rules and associated heuristic functions to support the cooperative architecture are specified. A protocol governing the knowledge exchange among local adjacent controllers is developed. A simulation-assignment modeling framework is used for assessing the effectiveness of this cooperative architecture under various levels of controller knowledge and network traffic congestion. The cooperative decentralized system is tested under various scenarios of knowledge and cooperation and network traffic demand levels. The cooperative system is compared against the shortest path algorithm as a benchmark.

Capacity of Multilane All-Way Stop-Controlled Intersections Based on the Conflict Technique

This paper presents an improved model for assessing the capacities of vehicular movements at all-way stop-controlled (AWSC) intersections with multilane approaches. In contrast to the current approach-based model incorporated in the 2000 edition of the Highway Capacity Manual (HCM), the proposed model simplifies the process of estimating the capacities of AWSC intersections and could be applied at any multilane AWSC intersections. On the basis of the conflict technique, a model was developed for estimating the capacities of vehicular movements at multilane AWSC intersections under three traffic demand levels: under-saturated, partially saturated, and fully saturated. Model parameters were calibrated by field data collected from 41 AWSC intersections with various lane configurations throughout the United States. The effectiveness and reliability of the proposed model were verified in comparison with the methodology of HCM 2000 with various lane configurations and traffic demands. The proposed model addresses the major limitations of the current HCM 2000 methodology, which deals only with the conditions of two lanes on each approach at AWSC intersections.

Energy-Efficient Self-Organizing Routing for Wireless Mobile Networks

The instant deployment without relying on an existing infrastructure makes mobile ad hoc networks (MANET) a striking choice for many dynamic situations. An efficient MANET protocol may be applied to other important emerging wireless technologies, such as wireless mesh and sensor networks. This article proposes a hierarchical routing scheme that is scalable, energy efficient, and self-organizing. The new algorithm that is discussed in this article is the Dynamic Leader Set Generation (DLSG). This algorithm dynamically selects leader nodes based on traffic demand, locality, and residual energy level, and de-selects them based on residual energy. Therefore, energy consumption and traffic load are balanced throughout the network, and the network reorganizes itself around the dynamically selected leader nodes. Time, space, and message complexities are formally analyzed and implementation issues are also addressed. Incorporating the IEEE 802.11 medium access control mechanism and including the power saving mode, performance evaluation is carried out by simulating DLSG and four existing hierarchical routing algorithms. It shows that DLSG successfully extends network lifetime by 20 to 50% while achieving a comparable level of network performance.