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Signalized urban intersections are key components of urban transportation networks. They are traditionally viewed and designed as primarily motorized traffic facilities, and thus their physical and operational designs have traditionally aimed at maximizing traffic throughput subject to constraints dictated by vehicular safety requirements and pedestrian crossing needs. Seen from a holistic viewpoint, urban intersections are hubs or effective centers of community activities of which traffic flow is only one. Those hubs have direct and indirect impacts on the overlapping traffic functionalities, the environment, public health, community wellbeing, and the local economy. This study proposes a new rating system, the Holistic Intersection Rating System (HIRS), aimed at appraising signalized intersections from a more inclusive viewpoint. This appraisal covers traffic functionality, sustainability, and public health and community wellbeing. This rating system can be used as a guide to conceive, plan, or design new intersections or revamp existing ones. HIRS rates signalized urban intersections based on the level of use of relevant enabling technologies, and the physical and operational designs that allow those intersections to operate holistically, thus leading to a more human-centric and sustainable operational performance. HIRS was validated using a panel of experts in construction, transportation, and public health. The Relative Importance Index (RII) method was used to weigh the HIRS features. The rating system was piloted on a sample of 20 intersections in different cities in the UAE. The results revealed glaring gaps in services to or the consideration of pedestrians, cyclists, and nearby households. The sample intersections scored a mean of 32% on the public health and community wellbeing section, 37% on the pedestrian subsection, and 15% on the cyclist subsection. Such relatively low scores serve as indicators of areas for improvements, and if mapped to their specific features and their relative weights, specific physical and operations designs and technology integration can be identified as actionable items for inclusion in plans and/or designs.

Climate change is a major issue that has serious disastrous implications on the environment, which if continues on the same pattern, will cause severe consequences. One of the leading causes of climate change is the emissions of harmful gases, especially CO2. In 1997 the Kyoto protocol was signed where 192 countries created a system that monetizes CO2 emissions with an aim to control them. A carbon trading scheme was signed by most countries, but the scheme fell short of its goals due to corruption, manipulation, and lack of integrity. This work presents an improved blockchain-based approach to achieve the objective of reducing carbon emissions. Blockchain’s distinct features such as security, immutability, transparency, traceability, and trust, make it a robust and reliable solution for the in-hand carbon trading market. Previous Blockchain-based proposals were not comprehensive, practical, applicable, or efficient enough to form an effective solution. This research addresses the current gaps and proposes a comprehensive framework that employs smart contracts in Blockchain of Things (BoT) to ensure integrity in the system and reach fair trade status that favors the environment over companies’ cost reductions and profit-making. The result is an optimized carbon emission trading framework, fully transparent with automated trading and control mechanisms.

Economic growth is an important goal for governments. This growth is promoted by economic activities known to be a major emitter of greenhouses gases resulting in climate change. Most of those activities are related to logistics and supply chain sectors. Countries around the world implemented an initiative to control and reduce GHG especially CO 2 under a monitory system to trade carbon emission allowances. However, the CET suffered from shortages that degraded its effectiveness. Also, it was not comprehensive enough to cover all sources of CO 2 emissions. Many researchers proposed solutions to elevate the present CET scheme in which the technology of blockchain was utilized. Blockchain presented itself as an ideal solution for the current CET market due to its characteristics of transparency, immutability, security, and decentralization. However, non of the previous work was adequate or comprehensive enough. This research proposes a comprehensive blockchain-based framework to support and CET that integrates IoT devices and includes logistics and supply chain sectors. Our framework collects full data about CO 2 emissions directly from all sources and shares them securely and immutably among stakeholders. It enhances making decisions on economic processes that reduce CO 2 emission as well as trading allowances.

Design-hour volume (DHV) and directional DHV (DDHV) are important traffic forecast parameters for both planning and operational studies. They are used for roads and intersection design and operational analysis. Estimating these two parameters requires a record of hourly volumes for every hour in a year. Therefore, permanent traffic counters are usually used to keep a record of those hourly volumes. The use of permanent counters faces several challenges because of adjacent construction activities and hardware or communication failure. These challenges result in the missing part of the collected data. Moreover, estimating DHV and DDHV based on short-term traffic counts is often needed. In this research, an artificial intelligence approach is used to estimate DHV and DDHV for roadways with different functional classifications. An artificial neural network model, which utilises historical records of annual average daily traffic along with other road characteristics, such as number of lanes and functional classification, is developed. Results show that the model was able to achieve a highly accurate and reliable DHV and DDHV estimates.

Climate change is a major issue that has disastrous implications on the environment, which, if continued, will cause severe consequences. One of the leading causes of climate change is the emissions of harmful gases, particularly CO2. In 1997, the Kyoto protocol was signed by 192 countries, creating a system that monetizes CO2 emissions with an aim to control them. Most countries signed a carbon trading scheme, but the scheme fell short of its goals due to manipulation, lack of integrity, and multiple other challenges. This work presents an improved blockchain-based approach to achieve the objective of monitoring the reduction of carbon emissions. Blockchain's distinct features such as security, immutability, transparency, traceability, and trust, make it a robust and reliable solution for the carbon trading market. Previous Blockchain-based proposals were not comprehensive, practical, applicable, or efficient enough to form an effective solution. This research addresses the current gaps and proposes a comprehensive three-stage hierarchical blockchain framework that employs smart contracts in Blockchain of Things (BoT) to ensure integrity in the system and reach fair trade status that favors the environment over companies' cost reductions and profit-making. The result is an optimized carbon emission trading framework, fully transparent with automated trading and control mechanisms.

Transit signal priority (TSP) has gained popularity in providing public transportation buses with preferential treatment at signalized intersections. Many studies have addressed its implementation in prompting enhanced public transportation service, such as reducing person delay and reducing transit travel time. However, most TSP implementations are done at the intersection level. Only a few studies have addressed the problem of integrating signal priority in coordinated real-time traffic signal control systems. A particular problem in this case is the uncertainty of predicting transit movements when considering the variability of dwell times at service stops. This study presents the development of a real-time traffic signal control integrating traffic signal timing optimization and TSP control using genetic algorithms (GA) and artificial neural networks (ANN) modeling. The GA is used to find near-optimal signal timings. Six different signal control systems were evaluated: fixed-time control with and with...

The focus of this chapter is the impacts of urban transport on health: physical, mental, well-being, and livability. Specifically, and besides the traditionally obvious and well-studied subjects of transport-induced crash injuries and pollution health impacts, the chapter presents the modern day non-contagious ailments such as stress, cardiovascular diseases, physical inactivity, and obesity and highlights their links and interactions with urban transport systems. These health impacts are mostly significant in the long term. They are subtle, slow, and pervasive. They have traditionally not been addressed or even identified as transport related; only recently and just qualitatively, these impacts came to the fore of the interrelated subjects of urban form, transport, and modern day health problems. The chapter will identify the needs for research to both further the understanding of the dynamics between transport and urban form on one hand and the identified health impacts on the other. The chapter will also address the question of how to objectively measure and quantify these health impacts with a particular view to evaluating the benefits that can be realized through low carbon mobility solutions.

Analytical macroscopic models are presented to quantify delay at signalized intersections for extended control periods (multiple cycles) attributable to downstream time-variable congestion. Each of the models consists of an upstream demand estimation algorithm, a downstream queue build-up prediction algorithm, a shockwave propagation and dissipation tracking algorithm, and an algorithm that carries traffic aftereffect of a cycle’s control into the following cycle. Model inputs are basic traffic flow properties, signal control parameters, and link geometry. The models are modular and can be incorporated into any size system for one or multiple cycles. The models were tested on a hypothetical system of closely spaced intersections and under different traffic flow, control, and geometric conditions. Results show that delay induced by downstream traffic operations on an upstream intersection can be significant and that it may change with time and reach steadiness once traffic flow, downstream queues, and signal control measures stabilize and start replicating over time. Green ratios of neighboring intersections exert a significant influence on delay. For certain green ratios and geometric combinations, offsets can exert appreciable influence as well. The delay values and their temporal variation compared well with those from a microscopic simulation model.

Genetic algorithms (GAs) can be the tool of choice especially for optimizing combinatorial and complex problems in transport and infrastructure systems such as traffic signal control, pavement rehabilitation and design, and transit service scheduling. This paper presents an overview of different techniques to improve performance of GAs, with particular emphasis on parallel GAs (PGAs). Results are presented from applications of a simple GA (SGA) and a migration PGAs on a traffic control problem, a benchmark GA–difficult, and benchmark GA–easy problem. For all problems, savings in computation resources were realized when PGA was used. Advantages of PGAs are more pronounced for complex and difficult (deceptive) problems. On a difficult problem tested in this research, a PGA with four subpopulations was 7 times more efficient than a serial one, and a PGA with eight subpopulations was more than 18 times more efficient. With smaller and less complex problems, the impact of parallelism is less dramatic w...

This paper presents an alternative approach to predicting fire resistance of reinforced concrete (RC) T-beams that are both strengthened with Carbon Fiber Reinforced Polymers (CFRPs) plates and insulated with different protecting materials, and exposed to different fire scenarios. Based on both experimental and Finite Element (FE) studies, Artificial Neural Networks (ANNs) were used to extend earlier research. The developed ANN can be used to predict the fire endurance of RC T-beams strengthened with CFRP laminates when subjected to elevated temperatures. Different insulation thicknesses, materials types and fire curves were the input parameters in the presented ANN. The predicted fire endurance and time to failure are compared with obtained experimental and validated FE simulation results. Strong correlation between the predicted ANN, experimental, and FE results was obtained. The developed ANN model was then expanded in an extensive parametric study to predict the fire resistance of the strengthened beam using different insulation thicknesses, insulation material types, and fire scenarios. Design charts were also developed to be used as preliminary guidelines to aid designers in selecting the required insulation thickness for specific insulation systems and fire exposure scenarios. It was concluded that the developed and validated ANN could be used as a computational tool in the analysis and design of RC beams strengthened with CFRP plates and subjected to thermal fire loadings. Other conclusions and observations were also drawn.

Sustainability has emerged as a common theme in many processes related to urban life and the built environment. However, most engineering curricula still lacks the fundamentals of sustainable practices education that prepares graduating engineers to fulfill their expected roles and conduct future business in a more sustainable fashion. A more sustainable Civil Engineering curriculum requires a more systems approach to both diagnosing transport and built environment problems as well as devising solutions to such problems. This paper examines typical engineering curricula and identifies specific courses in the areas of transportation and construction where sustainability can be injected with minimum disruption to the structure of the course and/or its connection to other courses with the intended degree. For the two subspecialties, the core and elective courses are categorized and certain areas/modules within each course are identified, where the integration of sustainable engineering education is useful. Furthermore, some educational tools that facilitate the infusion of these concepts into the two subspecialties are presented.

This paper describes an experimental approach to test the suitability of micro-genetic algorithms (m-GAs) to solve large combinatorial traffic control problems and establishes relationships between time to convergence and problem size. A discrete time dynamical traffic control problem with different sizes and levels of complexity was used as a test-bed. Results showed that m-GAs can tackle computationally demanding problems. Upon appropriately sizing the m-GA population, the m-GA converged to a near-optimal solution in a number of generations equal to the string length. Results also demonstrated that with the selection of appropriate number of generations, it is possible to get most of the worth of the theoretically optimal solution but with only a fraction of the computation cost. Results showed that as the size of the optimisation problem grew exponentially, the time requirements of m-GA grew only linearly thus making m-GAs especially suited for optimising large-scale and combinatorial problems for online optimisation.

This paper presents travel time prediction models for both congested and non-congested conditions on urban arterial using only limited basic traffic data. The state-space notion of traffic processes and State-Space Neural Network (SSSNNet) models are used on simulation generated traffic data. Conditional Independence (CI) graphs are used to identify independence and interaction between observable traffic parameters thus only relevant ones can be used to predict travel time. Even with limited data, the predictive performance and computational efficiency of Conditional Independence Graphs coupled with State-Space Neural Networks are practically accurate. They also outperformed a traditional Artificial Neural Network model.

Background Transit oriented developments (TOD's) are a type of urban development that emphasize diversity in land use and concurrence of more sustainable modes of transport. Particular emphasis in TOD is given to active (Waling and Cycling) and semi-active (Transit) transport modes. The aim of the study is two-fold: demonstrates the advantages of transit-oriented developments including benefits to public health, and demonstrate a suitable analysis framework and structure for conducting similar studies. Methods A residential area at the outskirts of Dubai is used as a case study. Travel demand forecasting models were used to estimate changes in trip generation, distribution, mode choice, and route assignment brought about by the relocation of destinations. To account for normal variation and uncertainty, three travel mode choice shift scenarios and corresponding changes in distances walked and cycled were estimated based on published rates, local conditions and culture. Changes were mapped to health, environmental, energy, and economic benefits. On health, reductions in obesity, high blood pressure and high blood cholesterol, and heart attach were estimated based on published rates. Economic benefits (reduced mortality) were evaluated using HEAT (Health Economic Assessment Tool). Aggregate reductions in energy consumption, carbon dioxide emissions, travel time and operating costs (of private vehicles) were all estimated. Health benefits due to reductions in pollution were not estimated. Results A framework quantifying the health benefits of TOD was proposed and implemented. Travel demand changes were mapped to health benefits. The walk/cycle plus transit (to reach destination) scenario and yearly benefits due to reduced mortality was $1,797 and $8,343 for the conservative and optimistic scenarios, respectively; for the Walk/Cycle the values are $10,973 and $50,941. That a specific amount of health-induced economic gain may be estimated in conjunction with TOD's using the proposed framework is the more relevant outcome. Conclusions Estimating health gains, environmental improvements, and economic benefits of TOD implementation were possible using standard travel demand forecasting models. Mapping travel changes to health and other benefits is key to quantifying different benefits of TOD's. Normal variation and uncertainty precipitated by variations in policies, local environments and cultures may be accounted for by considering a range of scenarios.

Background Urban roadway intersections are key components of urban transportation networks. They are traditionally viewed and designed as primarily motorized-traffic facilities thus their physical and operational designs aimed at maximizing traffic throughput. Seen from a systems’ viewpoint, however, they are transport and community facilities with direct and indirect impacts on traffic functionalities, public health and the livability of communities. Thus, an integrated physical and operational design approach is necessary to design and/or analyze those intersections. Methods This study proposes a new methodology for measuring intersection performance called the Holistic Intersection Rating System (HIRS). It is based on design and technology innovation to improve operations and foster public health and community well-being. HIRS will assess the intersection design and functionalities with respect to two main categories: enabling technologies and physical design aimed at the transport functionality, and physical design and operability aimed at public health and community well-being. In particular, HIRS examines transportation intersections as system components of the urban environment. It rates the physical features of the road and the different motorized and non-motorized traffic flow design and operation parameters that equitably optimize throughput and support desired public health and community well-being outcomes. The public health and community well-being outcomes HIRS rates relate to physical and mental health, community severance, and “completeness” of intersections. Results HIRS facilitates pedestrian and cyclists’ use of intersections so intersections serve the dual needs of meaningful travel and good health while improving drivers experience and neighborhood livability and quality of life. The HIRS index may be constructed using common intersection travel efficiency and public health and community well-being indicators or indicators. The indicators can be weighed using two methodologies: the Relative Importance Index (RII) and the Analytical Hierarchy Process (AHP). The proposed system will be validated using an interdisciplinary industry panel of experts, and with empirical theoretical data runs. Conclusions Physical and operational design of roadway intersections, and the rating of sufficiency of such designs, are both necessary and possible for the well-functioning and use of urban transport systems and their users irrespective of modality. Tools that are dynamic and accommodating of changing designs and community values and priorities are necessary.

Accurate estimates of intermediate signal traffic output is necessary for designing meaningful multi-cycle intelligent or dynamic signal control in congested conditions. Models are presented that capture traffic output of intersections in congested interrupted flow conditions with explicit consideration of interactions, over space and time, between traffic streams at successive signals. Three unique operational characteristics along with their interactions were found to characterize distinct traffic flow regimes and determine system output. Models to predict the formation and intensity of these operational characteristics and the corresponding flow regimes were derived. Results demonstrate that an accurate representation of these regimes provides critical information for subsequent control steps.

Traffic crashes are the obvious and tragic outcome of a complex process of which traffic, human factors, roadway engineering and surrounding environment are all contributing factors. Traffic operations, however, inflict more physical and psychological harm on human health, albeit, it is not immediately obvious as crashes. Some of those harms show symptoms over longer terms, possibly a lifetime. That, however, does not change the fact that such harms are serious infractions of public health and community wellbeing. Is there a case for traffic medicine to expand scope and involve coverage of other traffic harms? Literature and studies have now clearly demonstrated the strong direct and indirect influence of transport planning, design, and operations‐ including travel choices and traffic operations‐‐on public health including traffic crashes. Beyond crashes, long term-health impacts of transport decisions and practices are now established facts. This talk will make the point that the scope of traffic safety need to move beyond the prevention (and management) of serious crashes and injuries to the promotion and adoption of transport policies and traffic operation measures that, in addition, preserve and promote public health and community wellbeing in a broader sense. It is argued that such policies and measures will, in the mid‐ to long‐term and as a matter of necessity and logical connectedness, result in travel decisions--as in trip making and mode and route choices--and traffic operation measures--as provision of space and junction control--that, collectively, foster calmer and more equitable thus "healthier" traffic operations. The result is both short‐ and long‐term public health improvements including reduced risks of serious crash injuries. A case study of health impacts of traffic congestion is presented. While reduction of serious crash is an urgent need the world around, the notion of traffic polices and operations as a tool to promote public health is not as obvious nor has it been part of the domain of traffic/transportation engineering or planning. The case is made that traffic operations impact public health and community wellbeing well beyond crashes, and that the professions of traffic/transport and public health collaboratively must adopt a more holistic approach to traffic safety and health: one that goes beyond immediate concerns with serious traffic crashes to one that promotes long‐term healthy living of which managing serious crashes is only a part. Towards this end, changes in policies, culture, education, planning, engineering, and systems operations will be necessary, as is a systems approach. 0 Language: en

Prediction of the pavement distress index (DI) for different road sections on a network scale over an extended time horizon is necessary for efficient maintenance planning and resource allocation. In many cases, however, sufficient information on causal factors of pavement distress is unavailable. Nonetheless, models with sufficient reliability have to be developed in these situations. It is shown in this research that with suitable statistical models and application, sufficient information on missing causal factors may be accounted for indirectly by incorporating preceding observed DI values as an independent variable when predicting future DI values. Among different statistical modeling approaches, autoregression models with recursive applications were shown to produce reliable DI predictions. Extended future DI values were projected recursively by first predicting DI values for the immediate future interval based on current pavement age and DI value, and then using the new predicted DI value to predict...

Macroscopic models that can be used to estimate the delay at signalized intersections caused by downstream traffic disturbances, such as queues in closely spaced settings, are presented. The models use basic traffic flow properties and control parameters at neighboring intersections. The models are applied to a hypothetical two-signal system. The results show that the delay caused by downstream disturbances can be significant and therefore must be included in level-of-service and capacity analyses. The results also show that control parameters, such as offsets and green splits at upstream and downstream approaches, play key roles in reducing delay. Validation by the use of microscopic traffic simulation shows close agreement between the delay estimates from the proposed models and that from the microscopic simulation model.

Shoulder rumble strips and enhanced pavement markings were two key areas identified by AASHTO to reduce runoff-road collisions. The Michigan Department of Transportation is currently testing the effectiveness of placing shoulder rumble strips close to the edge of travel lanes and painting a line on the rumble strip to improve the retroreflective properties of the line. Two issues arise from placing the rumble strips closer to the edge line and moving the paint line onto the rumble strip: the potential increased noise when vehicles crossed over the rumble strips and damage to the pavement if vehicles moved closer to the construction joint between the travel lane and the shoulder. This study found that simply moving the paint line onto the rumble strips resulted in vehicles moving slightly closer to the edge of the travel lane. However, maintaining the current edge line and adding an additional paint line on the rumble strip resulted in vehicles moving away from the edge of the pavement and thus reduced noise and potential damage to the pavement.

This paper presents a procedure for dynamic design and evaluation of traffic management strategies in oversaturated conditions. The method combines a dynamic control algorithm and a disutility function. The dynamic algorithm designs signal control parameters to manage formation and dissipation of queues on system links with explicit consideration of current and projected queue lengths and demands. The disutility function measures the relative performance of the dynamic control algorithm based on preset system performance goals. The user may statically select the management strategy, or alternatively the system may be instructed to set off different management schemes based on predefined performance thresholds. The problem was formulated as one of output maximization subject to state, control, and traffic management strategy choices. Solutions were obtained using genetic algorithms. Four traffic management plans were tested to show the capabilities of the new procedure. The results show that the procedure is able to generate suitable signal control schemes that are favorable to attaining the desired traffic management goals. The results showed that multiple, or hybrids of single measures of effectiveness may need to be examined in order to correctly assess system performance. The procedure has potential for real-time implementation in an intelligent transportation system setting.

This paper demonstrates the feasibility of an advanced concept of traffic control for congested urban arterials in an intelligent transportation system (ITS) setting. The core idea of the new concept is to integrate the capability of dynamic adaptive signals with dynamically optimized time-dependent variable speed. Under such control, speeds would be automatically optimized and set by a central computer in parallel with the other signal control parameters. Speed and signal control parameters would change between links and over time, in response to changing traffic conditions. Drivers would follow the optimized speed as they enter a link. Once an optimal speed has been set for a link, it remains constant until the control cycle ends. Link speeds would be updated only at the end of every control cycle. The control cycle may change in length as system conditions evolve. The new control concept was tested on a congested arterial with multiple links. The arterial system was modeled as a discrete event time var...

Background In the context of city operations and management, smart generally implies“…turning to technology to better connect our communities, improving citizen services”, and, more importantly, finding more intelligent ways to manage city resources and assets. It is a common notion that a city transportation system is a critical asset that must be intelligently managed to enable a better quality of life. It is less clear, however, that a city transportation system is in fact a public health tool and enabler. A transportation system, though, is not just the physical system it is; it is the integration of several activities some of which involve physical assets while other involve policies, standards and norms, that take place over long time span and involve significant resources including technology. Catering for public health should be an integral part of the smart city movement and thinking. Methods The paper first examines the different activities involved in putting together a transportation system, namely: Planning, Design, construction, Operations (management), Maintenance, and, in some cases, Decommissioning (henceforth PDCOMD), and then maps those activities to known public health outcomes. The paper notes that certain activities of the transport system development process are more suited to serve public health than others. For one, transportation system planning is fundamental to ensuring suitable integration of public health in the early stages of putting the transport system together. The operations activity is equally critical but historically—and to this day-- seldom viewed or used as the public health tool it is. Results This paper shows that “smart” as in integration of information and communication technology to improve efficiency of the transportation system may or may not serve public health; public health considerations in some cases require the calming/slowing down and spatial shifting-- thus the appearance of inefficiency-- of certain elements of the transportation system. Alternatively, the notion of efficiency as applied to transportation systems may need to be modified to explicitly incorporate aspects of public health. Conclusions The now trendy notion that information sensing, connectivity, and computation form the backbone of smart cities is questioned. The paper presents an alternate framework where public health is made integral to the notion of smart cities through explicit consideration and integration in key transportation system activities. Adopting an encompassing definition of “health” and “healthy” is vital. Among the many transport system activities, the paper identifies two, planning and operating, where public health consideration is particularly consequential.

The formulation and solution of a dynamic signal control and queue management algorithm for two-way oversaturated arterials are presented. The algorithm is structured to find optimal control and queue management in at least one direction: the primary direction. The other direction is optimized only subject to the fulfillment of the constraints on the primary direction. The main idea of the procedure is to manage queue formation and dissipation through proper design of signal control parameters such that queues are always contained within respective links and that spillbacks are prevented. The two-way control procedure presented accounts for all possible traffic flow regimes that may form as a result of queue management schemes. Genetic algorithms were used to solve the problem. The results show that although the algorithm provides optimal control and queue management in the primary direction, it was also able to manage queues in the secondary direction so that the occurrence of queue spillbacks is prevented. Progression was attainable only in the primary direction. However, the algorithm was able to effectively deal with evolving traffic queues in both directions. In all but one link, traffic was successfully contained in the respective links, thus preventing spillback.

Models for estimation of the capacities of oversaturated arterials were developed. The input variables in these models are capacities of individual intersections, offsets, and vehicle queue lengths. Models for quantification of capacity loss due to blockage caused by downstream queues are also presented. The proposed models show that when arterial capacity is determined in oversaturated conditions, it is not sufficient to consider only the capacities of critical intersections; instead, the capacities of critical subsystems must be considered. A critical subsystem is any two intersections plus the link that joins them where traffic processing capability is the lowest. This traffic processing capability, or critical subsystem capacity, determines the arterial capacity. It is a function of the capacities of the respective intersections, the offset between them, and the queue length on the link joining them. It is shown that a critical subsystem is not unique in that it may change location over the course of the study period. To minimize capacity loss, it is shown that offsets must be an explicit function of queue lengths. The practical use of the models was demonstrated for an oversaturated two-intersection system. The results show that improper setting of offsets can lead to significant capacity loss. In extreme cases all capacity in a given cycle may be lost if the offsets are not set properly.

The issues of micro-genetic algorithm (micro-GA) convergence, population size, internal variability, and performance are examined. Procedures are developed to determine best population size when micro-GAs are used for optimization of real-world problems. Results show that the best population size cannot be determined before convergence has occurred and that convergence may occur without reaching an optimal solution. To determine the best population size, this article proposes the use of consistency of the final fitness values in addition to the fitness value at convergence. Internal variability must be considered in finding the best population size, and that best population size depends on the level of computational resources used. Smaller populations with generous computational resources performed as good as and sometimes better than larger populations. Midsized populations consistently exhibited lower variation in fitness value. For a given population size, the increase in fitness value may not be worth the added computation cost beyond a certain point. Three approaches are proposed to determine the best population size: (1) a random selection approach, (2) a detailed approach, and (3) a simplified approach. We used the detailed approach with our traffic problem and found that, with sufficient computational resources, the micro-GA performed best with population size around the square root of the string length. We tested this proposition on standard simple and deceptive problems and found it to hold true.

Emerging artificial intelligence techniques such as genetic algorithms (GAs) allow a more realistic representation and solution of difficult and combinatorial problems such as the dynamic traffic queue management problem. Computational experience in solving such complex large-scale problems by use of micro-GAs is described. In addition to providing evidence of the ability of micro-GAs to successfully identify optimal traffic management schemes, some micro-GA-associated computational issues that warrant attention are highlighted. Choosing a proper population size is a critical decision, and internal variability must be accounted for to assess the goodness of the optimization results properly. A simple rule for deciding the best population size for micro-GAs is proposed. Micro-GAs may converge to low-quality solutions, particularly with very small population sizes; convergence of micro-GAs by itself is not a sufficient indication of good performance. The size of the search space for some real-world systems can pose some difficulties to micro-GAs. Choosing between micro-GAs and regular GAs is a problem-dependent decision.

The formulation and solution of a new algorithm for queue management and coordination of traffic signals along oversaturated arterials are presented. Existing traffic-control and signal-coordination algorithms deal only with undersaturated steady-state traffic flow conditions. No practical algorithms are readily available for oversaturated flow conditions. The main idea of the procedure is to manage queue formation and dissipation on system links so that traffic flow is maximized by efficiently using all green time, preventing formation of de facto red, accounting for the non-steady-state conditions, and providing time-dependent control measures. The problem is formulated as a throughput maximization problem subject to state and control variables. The solution is then obtained using genetic algorithms. The results show that the control procedure can produce dynamic and responsive control so that traffic progression is attained and all undesirable conditions such as queue spill-back and de facto red are avoided.

Models to estimate right-turn-on-red (RTOR) volumes at intersections with exclusive right-turn (RT) lanes are developed, and the effects of RTOR volumes on computed delay are assessed. The important variables in these models are the RT volume, followed by green-time-to-cycle (G/C) ratio, volume of conflicting traffic, and whether there is a protected phase for opposing left-turning vehicles. The estimated RTOR increased as the RTs increased. However, it decreased as G/C and the volume of conflicting traffic increased. Results show that not accounting for RTOR volumes can lead to a significant difference in delay estimates for RT lanes and, to a lesser extent, on the corresponding approaches. For RT lanes, in one-half of the cases the difference was greater than 5 percent, in more than one-quarter of the cases the difference was greater than 10 percent, and in at least one of eight cases the difference was greater than 20 percent. Differences for individual cases ranged between 0 and 130 percent, with an average of 12 percent. For approaches, the average delay difference was 4 percent, and for individual cases the difference ranged between −2 and 78 percent. As recommended by the Highway Capacity Manual, actual field counts of RTOR volume should be used whenever available. However, in the absence of such counts, the models developed here can be used and hence should be considered in capacity analysis procedures.

The Design-Hour Volume (DHV), which is defined as the 30th highest hour volume in a year, is a significant concept in transportation engineering and planning. Finding the DHV requires hourly traffic counts for an entire year. However, this becomes a challengeable task when part of the data is not collected because of different reasons, such as construction activities or hardware failure. In this paper, an Artificial Neural Network (ANN) approach is used to develop a DHV prediction model based on historical traffic counts. The model takes into account the correlation between DHV and other variables such as AADT, functional classification, and number of lanes. Results show that the ANN model is capable of providing accurate and reliable DHV estimates.

Transit signal priority (TSP) is a control strategy that provides preferential treatments for transit vehicles. This strategy has shown potential to minimize delay with negligible or slight impact on non-transit vehicles. It is also an important strategy for improving and promoting schedule adherence and service reliability. This paper shows a microscopic simulation study to identify the impact of implementing TSP strategies on an arterial corridor segment at Michigan State University. The chosen segment is one of the busiest corridors on campus. It experiences vehicular traffic, pedestrian movements, and transit routes. It has four signalized intersection, and one pedestrian semi-actuated signal. Three peak periods were modeled; morning, midday, and evening. Microscopic simulation modeling is done in VISSIM microscopic simulation environment. TSP functions are programmed using vehicle actuated programming (VAP) to evaluate different TSP parameters, such as maximum green extension and red truncation. Simulation results show that transit vehicles benefit from the provided preferential treatment. On the other hand, TSP has minimal negative impact for on general traffic. In terms of TSP impact on pedestrians, TSP has no negative impact on pedestrians. The study also shows that the expected benefits from TSP depend on the congestion level, where less benefit could be gained when the congestion level is higher.

This study investigated the effect of driver and vehicle characteristics on vehicular speeds on a selected tangent rural section of two lane two way highway (speed limit = 80 km/h) in Jordan. The study found that, drivers had an overall average speed of 85.86 km/h with a standard deviation of 17.34 km/h. Older drivers were found to have lower speeds than younger drivers. Male drivers had higher but not significantly different mean speeds than female drivers. Seat belt users had higher mean speeds than non-seat belt users. Considering vehicle type, passenger car were found to have the highest speeds, followed by pickups, busses, vans and trucks respectively. Newer vehicles had higher speeds than older vehicles. Vehicular speeds were inversely proportional to the number of occupants in the vehicle. Loaded pickups and trucks had lower speeds than unloaded ones.

The idea of using speed as a control variables (as opposed to being a constraint) that can be optimized dynamically and used in controlling signalized networks is explored in this paper. Such speed is an operating speed, not a speed limit. The paper demonstrates how control with dynamic operating speed can be used to realize different control objectives including improved traffic flow and safety. The analysis shows that using dynamic speed control (DSC) in signalized networks can improve operation of signalized networks by increasing throughput, decreasing delay and number of stops, and reducing speed noises and speed variation thus lowering the likelihood of accidents. Using dynamic speed control synchronously with dynamic traffic signals in signalized networks can enable more sustainable traffic operations: higher throughput, safer, fuel-conservative, reduced-emissions signalized network systems. As physical capacity of networks and signal-only improvements are reaching a plateau and networks’ physical expansion being a no-option, it is time to embrace new control concepts. As information availability improves (through initiatives such as IntelliDrive) and advances in wireless communications, computation, sensor technologies and their deployment in intelligent transportation system settings mature, it is now possible to design and implement advanced control concepts. Dynamic speed control systems which communicates the near-optimal speed optimized based on real-time data to guide drivers on their best speed selection is one such concept.

Traffic delays are still a significant part of the daily commutes in most urban areas despite significant investments in infrastructure. Policy, design and operational measures are usually taken to reduce emissions from mobile sources by reducing congestion, promoting modal diversity, or using advanced technologies. Although reduced emissions and improved fuel efficiency have resulted from proper traffic control, municipalities and public agencies often overlook the need for better traffic control through revised control schemes. Simple changes to traffic control schemes and/or incremental geometric designs improvements is all that is needed to improve traffic system performance without the need for expensive and highly advanced technologies. In this paper, the issue of using proper signal timing plans at signalised intersections and its effect on traffic operations and environment is highlighted. A real-life example is studied. Careful considerations to signal timings parameters resulted in lower traffic delay, reduced emissions, and better fuel consumption efficiency. Copyright 2016 Inderscience Enterprises Ltd. Scopus