Prediction of vehicle activity for emissions estimation under oversaturated conditions along signalized arterials

The traditional methodology for estimating vehicle emissions based on vehicle miles traveled and average speed is not reliable because it does not consider the effects of congestion, control devices, and driving mode (cruise, acceleration, deceleration, and idle). We developed an analytical model to predict vehicle activity on signalized arterials with emphasis on oversaturated traffic conditions. The model depends only on loop detector data and signal settings as inputs and provides estimates of the time spent in each driving mode, which consequently leads to more accurate vehicle emission estimates. The application of the proposed model on a real-world arterial shows that it accurately estimates the time spent and consequently the emissions per driving mode. We also applied the model to evaluate the effectiveness of signal timing optimization in reducing vehicle emissions

A Real-time Signal Control System to Minimize Emissions at Isolated Intersections

Continuous transportation demand growth in recent years has led to many traffic issues in urban areas. Among the most challenging ones are traffic congestion and the associated vehicular emissions. Efficient design of traffic signal control systems can be a promising approach to address these problems. This research develops a real-time signal control system, which optimizes signal timings at an under-saturated isolated intersection by minimizing total vehicular emissions. A combination of previously introduced analytical models based on traffic flow theory has been used. These models are able to estimate time spent per driving mode (i.e., time spent accelerating, decelerating, cruising, and idling) as a function of demand, vehicle arrival times, saturation flow, and signal control parameters. Information on vehicle activity is used along with the Vehicle Specific Power (VSP) model, which estimates emission rates per time spent in each operating mode to obtain total emissions per cycle. For the evaluation of the proposed method, data from two real-world intersections of Mesogion and Katechaki Avenues located in Athens, Greece and University and San Pablo Avenues, in Berkeley, CA has been used. The evaluation has been performed through both deterministic (i.e. under the assumption of perfect information for all inputs) and stochastic (i.e. without having perfect information for some inputs) arrival tests. The results of evaluation tests have shown that the proposed emission-based signal control system reduces emissions compared to traditional vehicle-based signal control system in most cases

Characterizing Queue Dynamics at Signalized Intersections From Probe Vehicle Data

Probe vehicles instrumented with location-tracking technologies have become increasingly popular for collecting traffic flow data. While probe vehicle data have been used for estimating speeds and travel times, there has been limited research on predicting queuing dynamics from such data. In this research, a methodology is developed for identifying the travel lanes of the GPS-instrumented vehicles when they are standing in a queue at signalized intersections with multilane approaches. In particular, the proposed methodology exploits the unequal queue lengths across the lanes to infer the specific lanes the probe vehicles occupy. Various supervised and unsupervised clustering methods were developed and tested on data generated from a microsimulation model. The generated data included probe vehicle positions and shockwave speeds predicated on their trajectories. Among the tested methods, a Bayesian approach that employs probability density functions estimated by bivariate statistical mixture models was found to be effective in identifying the lanes. The results from lane identification were then used to predict queue lengths for each travel lane. Subsequently, the trajectories for non-probe vehicles within the queue were predicted. As a potential application, fuel consumption for all vehicles in the queue is estimated and evaluated for accuracy. The accuracies of the models for lane identification. queue length prediction, and fuel consumption estimation were evaluated at varying levels of demand and probe-vehicle market penetrations. In general, as the market penetration increases, the accuracy improves. For example. when the market penetration rate is about 40%, the queue length estimation accuracy reaches 90%. The dissertation includes various numerical experiments and the performance of the models under numerous scenarios

Transit Preferential Treatments at Signalized Intersections: Person-based Evaluation and Real-Time Signal Control

Efficient public transportation has the potential to relieve traffic congestion and improve overall transportation system performance. In order to improve transit services, Transit Preferential Treatments (TPT) are often deployed to give transit vehicles priority over other vehicles at an intersection or along a corridor. Examples of such treatments are exclusive bus lanes, queue jumper lanes, and signal priority strategies. The objective of this study is threefold: 1) perform a person-based evaluation of alternative TPTs when considered individually and in combination, 2) develop a bus travel time prediction model along a signalized arterial, and 3) develop a real-time signal control system, which minimizes total person delay at an isolated intersection accounting for stochasticity in transit vehicle arrivals. This study first develops analytical models to estimate person delay and person discharge flow when various spatial and time TPTs are present at signalized intersections with and without near-side bus stops. This part of the research has contributed to the modeling of traffic along signalized arterials by improving the previous models to evaluate various TPT strategies with and without nearside bus stops. Next, a robust method to predict bus travel time along a signalized arterial is developed. This part of the research contributes to the bus travel time prediction models by estimating the status of traffic signals using automated vehicle location (AVL) data. The model decomposes bus travel time along signalized arterials and infers trajectories of the transit vehicles. Finally, the real-time signal control system is developed to provide priority to transit vehicles by assigning weights to transit vehicle delays based on their passenger occupancies as part of the optimization objective function. The system optimizes the movements by minimizing total person delay at the intersection. The system estimates bus arrival time at the intersection stopline and uses the developed analyitical models in the first part of the research to evaluate the person delay measure. This part of the research contributes to the real-time signal control systems by providing a priority window to account for the stochasticity in bus arrival times

Traffic modeling, estimation and control for large-scale congested urban networks

Part I of the thesis investigates novel urban traffic state estimation methods utilizing probe vehicle data. Chapter 2 proposes a method to integrate the collective effect of dispersed probe data with traffic kinematic wave theory and data mining techniques to model the spatial and temporal dynamics of queue formation and dissipation in arterials. The queue estimation method captures interdependencies in queue evolutions of successive intersections, and moreover, the method is applicable in oversaturated conditions and includes a queue spillover statistical inference procedure. Chapter 3 develops a travel time reliability model to estimate arterial route travel times distribution (TTD) considering spatial and temporal correlations between traffic states in consecutive links. The model uses link-level travel time data and a heuristic grid clustering method to estimate the state structure and transition probabilities of a Markov chain. By applying the Markov chain procedure, the correlation between states of successive links is integrated and the route-level TTD is estimated. The methods in Part I are tested with various probe vehicle penetration rates on case studies with field measurements and simulated data. The methods are straightforward in implementation and have demonstrated promising performance and accuracy through numerous experiments. Part II studies network-level modeling and control of large-scale urban networks. Chapter 4 is the pioneer that introduces the urban perimeter control for two-region urban cities as an elegant control strategy to decrease delays in urban networks. Perimeter controllers operate on the border between the two regions, and manipulate the percentages of transfer flows between the two regions, such that the number of trips reaching their destinations is maximized. The optimal perimeter control problem is solved by the model predictive control (MPC) scheme, where the prediction model and the plant (reality) are formulated by macroscopic fundamental diagrams (MFD). Chapter 5 extends the perimeter control strategy and MFD modeling to mixed urban-freeway networks to provide a holistic approach for large-scale integrated corridor management (ICM). The network consists of two urban regions, each one with a well-defined MFD, and a freeway, modeled by the asymmetric cell transmission model, that is an alternative commuting route which has one on-ramp and one off-ramp within each urban region. The optimal traffic control problem is solved by the MPC approach to minimize total delay in the entire network considering several control policies with different levels of urban-freeway control coordination. Chapter 6 integrates traffic heterogeneity dynamics in large-scale urban modeling and control to develop a hierarchical control strategy for heterogeneously congested cities. Two aggregated models, region- and subregion-based MFDs, are introduced to study the effect of link density heterogeneity on the scatter and hysteresis of MFD. A hierarchical perimeter flow control problem is proposed to minimize the network delay and to homogenize the distribution of congestion. The first level of the hierarchical control problem is solved by the MPC approach, where the prediction model is the aggregated parsimonious region-based MFD and the plant is the subregion-based MFD, which is a more detailed model. At the lower level, a feedback controller tries to maximize the network outflow, by increasing regional homogeneity

Modelação interpretativa da segurança e emissões em corredores de rotundas e semáforos

Scientific research has demonstrated that the operational, environmental and safety performance for pedestrians depend on the geometric and traffic stream characteristics of the roundabout. However, the implementation of roundabouts may result in a trade-off among capacity, environmental, and safety variables. Also, little is known about the potential impacts for traffic from the use of functionally interdependent roundabouts in series along corridors. Thus, this doctoral thesis stresses the importance of understanding in how roundabout corridors affect traffic performance, vehicular emissions and safety for vulnerable users as pedestrians. The development of a methodology capable of integrating corridor’s geometric and operational elements is a contribution of this work. The main objectives of the thesis are as follows: 1) to analyze the effect of corridor’s design features in the acceleration patterns and emissions; 2) to understand the differences in the spatial distribution of emissions between roundabouts in isolation and along corridors; 3) to compare corridors with different forms of intersections such as conventional roundabouts, turbo-roundabouts, traffic lights and stop-controlled intersections; and 4) to design corridor-specific characteristics to optimize vehicle delay, and global (carbon dioxide – CO2) and local (carbon monoxide – CO, nitrogen oxides – NOX and hydrocarbons – HC) pollutant emissions. Vehicle dynamics along with traffic and pedestrian flow data were collected from 12 corridors with conventional roundabouts located in Portugal, Spain and in the United States, 3 turbo-roundabout corridors in the Netherlands, and 1 mixed roundabout/traffic-lights/stop-controlled corridor in Portugal. Data for approximately 2,000 km of road coverage over the course of 50 h have been collected. Subsequently, a microscopic platform of traffic (VISSIM), emissions (Vehicle Specific Power – VSP) and safety (Surrogate Safety Assessment Model – SSAM) was introduced to faithful reproduce site-specific operations and to examine different alternative scenarios. The main research findings showed that the spacing between intersections influenced vehicles acceleration-deceleration patterns and emissions. In contrast, the deflection angle at the entrances (element that impacts emissions on isolated roundabouts) impacted slightly on the spatial distribution of emissions. It was also found that the optimal crosswalk locations along mid-block sections in roundabout corridor was generally controlled by spacing, especially in the case of short spacing between intersections (< 200 m). The implementation of turbo-roundabout in series along corridors increased emissions compared to conventional two-lane roundabout corridors (1-5%, depending on the pollutant). By changing the location of a roundabout or turbo-roundabout to increase spacing in relation to upstream/downstream intersection resulted in an improvement of corridor emissions. Under conditions of high through traffic and unbalanced traffic flows between main roads and minor roads, vehicles along roundabout corridors produced fewer emissions (~5%) than did vehicles along signalized corridors, but they emitted more gases (~12%) compared to a corridor with stop-controlled intersections. This research contributed to the current state-of-art by proving a full comprehension about the operational and geometric benefits and limitations of roundabout corridors. It also established correlations between geometric variable of corridors (spacing), crosswalk locations or traffic streams, and delay, and CO2, CO, NOX or HC variables. With this research, it has been demonstrated that the implementation of a given intersection form within a corridor focused on minimizing CO2 may not be translated to other variables such as CO or NOX. Therefore, the develop methodology is a decision supporting tool capable of assessing and selecting suitable traffic controls according the site-specific needs.Estudos anteriores demonstram que os desempenhos operacional, ambiental e ao nível da segurança para os peões de uma rotunda dependem das suas características geométricas e dos fluxos de tráfego e de peões. Porém, a implementação de uma rotunda pode traduzir-se numa avaliação de compromisso entre as variáveis da capacidade, emissões de poluentes e segurança. Para além disso, a informação relativa às potencialidades de rotundas interdependentes ao longo de corredores é diminuta. Assim, esta tese de doutoramento centra-se na compreensão dos impactos no desempenho do tráfego, emissões e segurança dos peões inerentes ao funcionamento de corredores de rotundas. Uma das contribuições deste trabalho é o desenvolvimento de uma metodologia capaz de avaliar as características geométricas e operacionais dos corredores de forma integrada. Os principais objetivos desta tese são: 1) analisar o impacto dos elementos geométricos dos corredores de rotundas em termos dos perfis de aceleração e das emissões; 2) investigar as principais diferenças na distribuição espacial das emissões entre rotundas isoladas e em corredores; 3) comparar os desempenhos operacional e ambiental de corredores com diferentes tipos de interseções tais como rotundas convencionais, turbo-rotundas, cruzamentos semaforizados e interseções prioritárias; e 4) dimensionar um corredor de modo a otimizar o atraso dos veículos, e emissões de poluentes globais (dióxido de carbono – CO2) e locais (monóxido de carbono – CO, óxidos de azoto – NOx e hidrocarbonetos – HC). O trabalho de monitorização experimental consistiu na recolha de dados da dinâmica do veículo, e volumes de tráfego e pedonais. Para tal, foram selecionados 12 corredores com rotundas convencionais em Portugal, Espanha e Estados Unidos da América, 3 corredores com turbo-rotundas na Holanda e ainda um corredor misto com rotundas, sinais luminosos e interseções prioritárias em Portugal. No total foram recolhidos aproximadamente 2000 km de dados da dinâmica do veículo, num total de 50 h. Foi utilizada uma plataforma de modelação microscópica de tráfego (VISSIM), emissões (Vehicle Specific Power – VSP) e segurança (Surrogate Safety Assessment Model – SSAM) de modo a replicar as condições de tráfego locais e avaliar cenários alternativos. Os resultados mostraram que o espaçamento entre interseções teve um impacto significativo nos perfis de aceleração e emissões. No entanto, tal não se verificou para o ângulo de deflexão de entrada (elemento fulcral nos níveis de emissões em rotundas isoladas), nomeadamente nos casos em que as rotundas adjacentes estavam próximas (< 200 m). A implementação de corredores de turbo-rotundas conduziu ao aumento das emissões face a um corredor convencional de rotundas com duas vias (1-5%, dependendo do poluente). A relocalização de uma rotunda ou turbo-rotunda no interior do corredor, de modo a aumentar o espaçamento em relação a uma interseção a jusante e/ou a montante, levou a uma melhoria das emissões do corredor. Conclui-se também que em condições de elevado tráfego de atravessamento e não uniformemente distribuído entre as vias principais e secundárias, os veículos ao longo de um corredor com rotundas produziram menos emissões (~5%) face a um corredor com semáforos, mas emitiram mais gases (~12%) comparativamente a um corredor de interseções prioritárias. Esta investigação contribuiu para o estado de arte através da análise detalhada dos benefícios e limitações dos corredores de rotundas tanto ao nível geométrico como ao nível operacional. Adicionalmente, estabeleceram-se várias correlações entre variáveis geométricas do corredor (espaçamento), localização das passadeiras e volume de tráfego, o atraso, e emissões de CO2, CO, NOX e HC. Demonstrou-se ainda que a implementação de uma interseção ao longo do corredor com a finalidade de minimizar o CO2 pode não resultar na melhoria de outras variáveis tais como o CO ou NOX. Esta metodologia serve como apoio à decisão e, portanto, permite avaliar o tipo de interseção mais adequado de acordo com as especificidades de cada local.Programa Doutoral em Engenharia Mecânic

Enhanced Traffic Signal Operation using Connected Vehicle Data

As traffic on urban road network increases, congestion and delays are becoming more severe. At grade intersections form capacity bottlenecks in urban road networks because at these locations, capacity must be shared by competing traffic movements. Traffic signals are the most common method by which the right of way is dynamically allocated to conflicting movements. A range of traffic signal control strategies exist including fixed time control, actuated control, and adaptive traffic signal control (ATSC). ATSC relies on traffic sensors to estimate inputs such as traffic demands, queue lengths, etc. and then dynamically adjusts signal timings with the objective to minimize delays and stops at the intersection. Despite, the advantages of these ATSC systems, one of the barriers limiting greater use of these systems is the large number of traffic sensors required to provide the essential information for their signal timing optimization methodologies. A recently introduced technology called connected vehicles will make vehicles capable of providing detailed information such as their position, speed, acceleration rate, etc. in real-time using a wireless technology. The deployment of connected vehicle technology would provide the opportunity to introduce new traffic control strategies or to enhance the existing one. Some work has been done to-date to develop new ATSC systems on the basis of the data provided by connected vehicles which are mainly designed on the assumption that all vehicles on the network are equipped with the connected vehicle technology. The goals of such systems are to: 1) provide better performance at signalized intersections using enhanced algorithms based on richer data provided by the connected vehicles; and 2) reduce (or eliminate) the need for fixed point detectors/sensors in order to reduce deployment and maintenance costs. However, no work has been done to investigate how connected vehicle data can improve the performance of ATSC systems that are currently deployed and that operate using data from traditional detectors. Moreover, achieving a 100% market penetration of connected vehicles may take more than 30 years (even if the technology is mandated on new vehicles). Therefore, it is necessary to provide a solution that is capable of improving the performance of signalized intersections during this transition period using connected vehicle data even at low market penetration rates. This research examines the use of connected vehicle data as the only data source at different market penetration rates aiming to provide the required inputs for conventional adaptive signal control systems. The thesis proposes various methodologies to: 1) estimate queues at signalized intersections; 2) dynamically estimate the saturation flow rate required for optimizing the timings of traffic signals at intersections; and 3) estimate the free flow speed on arterials for the purpose of optimizing offsets between traffic signals. This thesis has resulted in the following findings: 1. Connected vehicle data can be used to estimate the queue length at signalized intersections especially for the purpose of estimating the saturation flow rate. The vehicles’ length information provided by connected vehicles can be used to enhance the queue estimation when the traffic composition changes on a network. 2. The proposed methodology for estimating the saturation flow rate is able to estimate temporally varying saturation flow rates in response to changing network conditions, including lane blockages and queue spillback that limit discharge rates, and do so with an acceptable range of errors even at low level of market penetration of connected vehicles. The evaluation of the method for a range of traffic Level of Service (LOS) shows that the maximum observed mean absolute relative error (6.2%) occurs at LOS F and when only 10% of vehicles in the traffic stream are connected vehicles. 3. The proposed method for estimating the Free Flow Speed (FFS) on arterial roads can provide estimations close to the known ground truth and can respond to changes in the FFS. The results also show that the maximum absolute error of approximately 4.7 km/h in the estimated FFS was observed at 10% market penetration rate of connected vehicles. 4. The results of an evaluation of an adaptive signal control system based on connected vehicle data in a microsimulation environment show that the adaptive signal control system is able to adjust timings of signals at intersections in response to changes in the saturation flow rate and free flow speed estimated from connected vehicle data using the proposed methodologies. The comparison of the adaptive signal control system against a fixed time control at 20% and 100% CV market penetration rates shows improvements in average vehicular delay and average number of stops at both market penetration rates and though improvements are larger for 100% CV LMP, approximately 70% of these improvements are achieved at 20% CV LMP

Multi-resolution Modeling of Dynamic Signal Control on Urban Streets

Dynamic signal control provides significant benefits in terms of travel time, travel time reliability, and other performance measures of transportation systems. The goal of this research is to develop and evaluate a methodology to support the planning for operations of dynamic signal control utilizing a multi-resolution analysis approach. The multi-resolution analysis modeling combines analysis, modeling, and simulation (AMS) tools to support the assessment of the impacts of dynamic traffic signal control. Dynamic signal control strategies are effective in relieving congestions during non-typical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. Although, an important aspect when modeling incident responsive signal control is to determine the capacity impacts of incidents considering the interaction between the drop in capacity below demands at the midblock urban street segment location and the upstream and downstream signalized intersection operations. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. A second model is developed to estimate the reduction in the upstream intersection capacity due to the drop in capacity at the midblock incident location as estimated by the first model. These developed models are used as part of a mesoscopic simulation-based DTA modeling to set the capacity during incident conditions, when such modeling is used to estimate the diversion during incidents. To supplement the DTA-based analysis, regression models are developed to estimate the diversion rate due to urban street incidents based on real-world data. These regression models are combined with the DTA model to estimate the volume at the incident location and alternative routes. The volumes with different demands and incident levels, resulting from DTA modeling are imported to a microscopic simulation model for more detailed analysis of dynamic signal control. The microscopic model shows that the implementation of special signal plans during incidents and different demand levels can improve mobility measures

Multi-resolution Modeling of Dynamic Signal Control on Urban Streets

Dynamic signal control provides significant benefits in terms of travel time, travel time reliability, and other performance measures of transportation systems. The goal of this research is to develop and evaluate a methodology to support the planning for operations of dynamic signal control utilizing a multi-resolution analysis approach. The multi-resolution analysis modeling combines analysis, modeling, and simulation (AMS) tools to support the assessment of the impacts of dynamic traffic signal control. Dynamic signal control strategies are effective in relieving congestions during non-typical days, such as those with high demands, incidents with different attributes, and adverse weather conditions. This research recognizes the need to model the impacts of dynamic signal controls for different days representing, different demand and incident levels. Methods are identified to calibrate the utilized tools for the patterns during different days based on demands and incident conditions utilizing combinations of real-world data with different levels of details. A significant challenge addressed in this study is to ensure that the mesoscopic simulation-based dynamic traffic assignment (DTA) models produces turning movement volumes at signalized intersections with sufficient accuracy for the purpose of the analysis. Although, an important aspect when modeling incident responsive signal control is to determine the capacity impacts of incidents considering the interaction between the drop in capacity below demands at the midblock urban street segment location and the upstream and downstream signalized intersection operations. A new model is developed to estimate the drop in capacity at the incident location by considering the downstream signal control queue spillback effects. A second model is developed to estimate the reduction in the upstream intersection capacity due to the drop in capacity at the midblock incident location as estimated by the first model. These developed models are used as part of a mesoscopic simulation-based DTA modeling to set the capacity during incident conditions, when such modeling is used to estimate the diversion during incidents. To supplement the DTA-based analysis, regression models are developed to estimate the diversion rate due to urban street incidents based on real-world data. These regression models are combined with the DTA model to estimate the volume at the incident location and alternative routes. The volumes with different demands and incident levels, resulting from DTA modeling are imported to a microscopic simulation model for more detailed analysis of dynamic signal control. The microscopic model shows that the implementation of special signal plans during incidents and different demand levels can improve mobility measures

Application of Metaheuristics in Signal Optimisation of Transportation Networks: A Comprehensive Survey

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.With rapid population growth, there is an urgent need for intelligent traffic control techniques in urban transportation networks to improve the network performance. In an urban transportation network, traffic signals have a significant effect on reducing congestion, improving safety, and improving environmental pollution. In recent years, researchers have been applied metaheuristic techniques for signal timing optimisation as one of the practical solution to enhance the performance of the transportation networks. Current study presents a comprehensive survey of such techniques and tools used in signal optimisation of transportation networks, providing a categorisation of approaches, discussion, and suggestions for future research