In a highway asset maintenance management system involving more than one highway asset type, identifying an equitable optimal allocation of available budget to individual asset systems is a major challenge. This is because the following outcomes are desirable: (1) the maintenance needs of all assets are adequately addressed, (2) the objectives of individual asset systems are optimally satisfied in an equitable manner, and (3) the objectives of the overall highway asset system are achieved optimally. To achieve these desirable outcomes, this paper proposes a two-stage approach in solving the dual-level multiasset, multiobjective pavement network maintenance optimal budget allocation problem. Stage I of the approach analyzes the individual multiobjective asset systems independently to establish for each a family of optimal Pareto solutions. Minimization of maintenance cost is selected as a common objective for the individual asset systems. This serves as the link for interaction with the Stage II analysis. Stage II adopts an optimal algorithm to allocate budget to individual assets by performing a cross-asset trade-off to achieve the optimal budget solution for the given overall system-level objectives. By defining a minimum performance threshold level for each asset type, the Stage II optimization analysis aims to achieve an equitable allocation of budget by maintaining equivalent amounts of performance improvements for the individual asset systems above their respective minimum threshold levels. The conceptual framework of the proposed approach is presented in this paper with a numerical example to illustrate the detailed working of the procedure.

Traffic speed is one of the basic variables that indicates the level of service of a road entity. It plays an essential role in transportation planning and management. This study attempts to establish a prediction model for speed distribution, in terms of average travel speed and standard deviation, using probe vehicle data in Hong Kong. Taking advantage of detailed traffic flow data obtained from the annual traffic census, a comprehensive traffic information database can be established using the geographical information system technique. The effects of traffic flow, road geometry, and weather conditions on speed distribution are determined using the Markov-chain Monte Carlo (MCMC) simulation approach full Bayesian method.

In this paper, a geometric model for predicting locations and times where sun glare has the potential to affect drivers on roadways was developed. Existing or prospective highway alignments can be considered. The method includes appropriate geometric models of the roadway alignment, astronomical computations for the relative position of the sun in the sky, and corrections for atmospheric refraction. Novel graphical tools were introduced to indicate the results of the model, which include sets of time and location where sun glare should be expected. The hypothesis that sun glare has an effect on congestion was tested by comparing real speed data from locations and times at which the geometric model indicates the presence of sun glare with those at which it does not. Sun glare is shown to have a statistically significant impact on mean traffic speeds.

Computer algorithms used to identify recurrent freeway bottlenecks have been studied since the deployment of loop detecting systems. Such algorithms automatically analyze the archived loop detector data and identify potential recurrent bottlenecks and their characteristics, such as location, time of day, and activation rate, for further investigation. In a highway congestion mitigation project, such algorithms can save time and resources for the initial screening of bottlenecks over a large freeway network. These algorithms include rule-based, contour-map-based, and simulation-based methods. However, existing methods require loop detector data with high accuracy and consistency, which is difficult to achieve in prevailing loop detecting systems. This paper proposes a new bottleneck identification algorithm with strong error and noise tolerance. Several simple denoising methods to improve the error resistance of existing algorithms are also proposed. Using statistical error analysis methods, the proposed algorithm and the denoising methods were calibrated and evaluated using field data collected from two distinct freeway corridors (US 12/14 and I-894) in the U.S. state of Wisconsin. Ground truth data for this study come from the manual inspection of 287,055 traffic video snapshots in the course of a month. In the evaluation tests, the proposed algorithm can produce quality congestion identification results with fewer false alarms than the existing algorithms, especially when identifying severe bottleneck congestion.

Public transportation systems play a significant role in emergency evacuation. Therefore, this paper is geared towards harnessing subway and bus transit to alleviate congestion pressure during evacuation of busy urban areas. Routing and scheduling of transit vehicles and subway operation is envisioned as a new variant of the well-established vehicle routing problem. The model presented in this paper combines multiple variants of the traditional vehicle routing problem while reflecting on the operational characteristics during emergency evacuation, to include (1) multiple depots to better distribute the transit fleet, (2) time constraints to account for the evacuation time window, and (3) constraints for pick-up and delivery locations of evacuees. The evacuation problem is hereafter defined as a multi-depot time-constrained pick-up delivery vehicle route problem. A framework, using constraint programming and local search methods, was developed to model and solve the problem. An optimal spatio-temporal evacuation model was performed first to optimize evacuation of background vehicular traffic, generating transit travel cost (i.e., link travel times) as an input to the evacuation problem. The methodology was applied to evacuate the entire city of Toronto. The results show that the Toronto Transit Commission fleet is capable of evacuating the transit-dependent population (1.34 million) within 2 h on average. The four subway lines of the city of Toronto carry approximately 0.62 million people and can evacuate these people in less than 3 h on average. Toronto Transit Commission shuttle buses (1,320 vehicles) can evacuate the remainder of the transit-dependent population (0.72 million) in approximately 1.5 h on average.

Until recently, the Poisson process has been used to model internet and road traffic queues. It has been established that internet traffic exhibits self-similarity, which is very different from the Poisson process. Motivated by this, efforts have been made to examine whether road traffic is also self-similar. Earlier efforts in this direction indicate that road traffic is indeed self-similar. To substantiate this, this paper examines, by various methods, whether real time traffic on a busy national highway in India is self-similar. The results from this examination prove that the traffic observed on the highway is self-similar. This paper also presents a novel method based on percentiles for computing the Hurst parameter, which is an indicator for the intensity of self-similarity. The paper also validates the percentile method with two other existing methods. Additionally, the traffic at a toll plaza on the highway has been modelled as queueing system, and performance measures have also been computed, namely, mean queue length and busy period distribution. The numerical results clearly demonstrate that the analysis presented in this paper can be useful for improved designs of toll plazas.

The need to understand the effects of diverting traffic is emphasized by growing congestion and delays. This paper examines incident-induced diversion behavior by using loop-detector data and incident records on a freeway in Virginia. This work diverges from previous studies by (1) addressing both existence of diversion and its magnitude, (2) relying on field data rather than surveys, and (3) statistically relating diversion behavior and magnitude to quantifiable incident characteristics and traffic conditions. A dynamic programming-based procedure is used to identify diversions by isolating transient level shifts, and the diversions are associated with incident and traffic characteristics and variable message sign (VMS) displays through a binary logit model. The magnitude of the diversion is statistically related to traffic conditions via a linear regression model. The models indicate that the probability of triggering a diversion increases when an incident lasts longer, more general-purpose lanes are blocked, and speeds are lower. The results on the effects of trip purpose/time and information availability are consistent with previous studies. The magnitude of the diversion, measured by diversion rate, is related to instant traffic flow characteristics, general traffic demand considerations, and the incident characteristics.

Highway agencies in the United States have started implementing SafetyAnalyst, highway safety management software. Some agencies have been developing or considering developing safety performance functions (SPFs) using local data to maximize the benefits of the software. With multiple years of data on hand, several models are available for developing the SPFs, yet there is no reliable guide to selecting appropriate models. To this end, this study examined 11 models, including 8 panel and 3 cross-sectional models, to recommend appropriate model types for intersection SPFs of SafetyAnalyst. Data collected from 18,356 intersections in Virginia over the period 2003–2008 were used. A cross-sectional model with summed or averaged crash frequencies was found to severely underestimate a dispersion parameter that plays a critical role in the empirical Bayes method. Panel models with independent correlation and autoregressive Order 1 correlation and a pooled cross-sectional model were found to be appropriate across all 8 subtypes. Agencies unable to construct panel data and develop panel models can apply the pooled cross-sectional model, which does not require matching the same intersections across different years.

Transport agencies around the world recognize the importance of on-time on-budget delivery of highway projects and understand the need for an efficient transport development process. Past studies of highway project duration and cost estimation were focused on construction, and few studies have focused on preconstruction project delivery from an agency’s perspective. This paper proposes the use of hazard-based duration models for investigating the potential risk factors that could affect project development time from planning to letting. A database containing over 35 years of highway project data from the state of Indiana was used to develop appropriate models. Of the three model forms (Weibull, Weibull with gamma heterogeneity, and log-logistic) considered in the paper, it was found that the log-logistic model is the most appropriate for modeling project development time. For each work category (pavement, bridge, and road/interchange projects), risk factors were identified, and their influences on project development time (and consequently, on letting time) were investigated. The developed hazard-based duration models were further applied to demonstrate their ability to predict expected project development time for delivery and the project-related factors that influence this duration, thereby providing the agency with a useful interpretation of associated delivery risks.

Road maintenance is essential to ensure a safe and efficient transportation system. Unfortunately, work zones can give rise to significant delays to road users. In this paper, two major limitations in the current work zone optimization models are addressed. First, we relax the assumption of determinism, and model vehicle arrivals as being stochastic. While previous work has shown that this relaxation is important in the quantification of user delay at work zones, no model exists that explicitly accounts for stochasticity in the optimization of work zones. Second, unlike in previous work in which idealized traffic flow modeling techniques have been used, the proposed model employs the traffic flow theory–based cell transmission model, yielding a more accurate and realistic representation of traffic flow dynamics. The focus in this paper is on two-lane two-way highways. A case study is presented to illustrate the proposed model.

Rural feeder service between villages and bus stops is largely a missing component in developing countries such as India. Traditionally, planning of rural feeder service has been carried out without due consideration to indirect costs associated with hard factors (e.g., walking distance and waiting time) and soft factors (e.g., crowding inside vehicles) of the service. A case study is presented for the planning of rural feeder service with due consideration to direct and indirect costs to users and operational viability of the service. Direct and indirect costs to users are expressed in terms of a comprehensive measure called generalized cost (GC). GC is developed on the basis of the willingness to pay (WTP) of rural commuters with respect to various attributes of rural feeder service. The work demonstrates the effect of catchment characteristics (i.e., number of villages, size of villages, and spatial separation represented by road network connecting villages to bus stop) on feeder service attributes (i.e., type of vehicle, route, headway of service and fare). It is also shown that planning of feeder service could be instrumental in bringing benefits to rural communities. The results presented in the paper are case specific, but the methodology can be applied to the planning of feeder services in other rural regions.