High-resolution Traffic Data Research Articles

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Spatiotemporal assessment of post-earthquake road network resilience using a data-driven approach

An accurate assessment of the spatial and temporal changes in the post-earthquake road network is essential for scientifically establishing emergency rescue programs and recovery strategies. This paper utilizes high-resolution traffic data to evaluate the resilience of city-to-city road networks to earthquakes. Additionally, it elucidates the distribution of link-level resilience in both temporal and spatial dimensions. Through a case study of the Luding earthquake, the results indicate that the road network's recovery duration exceeds five days, emphasizing the persistent and temporally delayed impact of earthquakes on traffic flow. Specifically, earthquakes disrupt link resilience, causing it to fluctuate around normal operational behavior. In the immediate aftermath of the earthquake, travel time from the epicenter to Luding surged by 132 %, triggering congestion that spread to neighboring cities. At the spatial level, spatial autocorrelation in link resilience is evident, with concentrated clusters of low-resilience links observed in segments such as G248 and S211. Our findings offer valuable and novel insights into the resilience of road networks to earthquakes from an empirical perspective.

Modeling of Freeway Real-Time Traffic Crash Risk Based on Dynamic Traffic Flow Considering Temporal Effect Difference

With the development of traffic detection facilities technology, it is currently possible to obtain high-resolution traffic flow data. Due to the particular driving characteristics of vehicles on freeways, once traffic crashes occur, they are generally with serious consequences, and hence traffic safety issues on freeways have always been popular topics. In order to better realize the change from static analysis after the crash to dynamic analysis before the crash toward freeway safety, as well as explore the relationship between dynamic traffic flow characteristics and real-time traffic crash risk under different temporal conditions, this research constructed a real-time traffic crash risk prediction model considering the temporal effect difference. First, traffic crash information and the matched big data of high-resolution traffic flow located on the section of milepost 100–130 of Interstate 5 (I-5) in Washington State, were extracted. In terms of temporal dimension, the research object was divided into weekdays and weekends, and the traffic state was divided into unsaturated and saturated. The random forest (RF) algorithm was introduced to identify the traffic flow variables of crash precursors, and support vector machine (SVM) was applied to build the traffic crash risk prediction model under the condition of temporal difference. A confusion matrix, receiver operating characteristic (ROC) curve, and area under curve (AUC) values were used to evaluate the accuracy of the model performance. Furthermore, the prediction performance of the proposed model was tested via constructing the risk model without consideration of temporal effect and traffic state difference. Finally, the rationality of variable screening was verified by inputting the data set without variable screening into the constructed model. The results showed that the occurrence mechanism of dynamic traffic crashes under different temporal effect conditions varies; the AUC values of the constructed prediction model were all between 0.7 and 0.9, indicating that the recommended model has good prediction accuracy. In conclusion, the real-time freeway traffic crash risk prediction model considering the temporal effect difference has certain advantages compared with the conventional model, and its performance is better than the prediction model without screening of important traffic flow variables. This approach can provide theoretical guidance for dynamic traffic safety management toward freeway under temporal difference conditions.

Full data imputation for freeway time-specific safety performance functions' estimation

Time-specific Safety Performance Functions (SPFs) were proposed to achieve accurate and dynamic crash frequency predictions. Unfortunately, some states do not have or archive the needed high-resolution traffic data to develop time-specific SPFs. This study proposes a novel iterative imputation method to impute the 100% missing volume and speed data from different states with similar crash rates. First, this study calculated the crash rates for 18 states and applied the One-Way Analysis of variance (ANOVA) test to group the states with similar crash rates. Second, as an example FL and VA, which both have traffic data, were used to test the proposed iterative imputation method. The results indicated that the imputed traffic data could capture the same traffic pattern as the real-collected traffic data. Further, the Mean Absolute Error (MAE) between the imputed Ln Volume and the real-collected Ln Volume for FL is only 2.47 vehicles for each segment for three hours. The MAE between the imputed Ln AvgSpeed and the real-collected Ln AvgSpeed for FL is only 1.36 mph. The Mean Absolute Percentage Error (MAPE) between the imputed Ln Volume and the real-collected Ln Volume is 11.07%. Meanwhile, the MAPE between the imputed Ln AvgSpeed and the real-collected Ln AvgSpeed is 7.40%. Finally, this study applied the proposed iterative imputation method to develop time-specific SPFs for the state without traffic data and compared the results. The results illustrated that the time-specific SPFs developed by imputed traffic data perfectly reflected the significant variables for both morning and afternoon peak models, with a prediction accuracy of 87.1% for the morning peak model.

Anatomy of perturbed traffic networks during urban flooding

Urban flooding disrupts traffic networks, affecting mobility and disrupting residents’ access. Flooding events are predicted to increase due to climate change; therefore, understanding traffic network’s flood-caused disruption is critical to improving emergency planning and city resilience. This study reveals the anatomy of perturbed traffic networks by leveraging high-resolution traffic network data from a major flood event and advanced high-order network analysis. We evaluate travel times between every pairwise junction in the city and assess higher-order network geometry changes in the network to determine flood impacts. The findings show network-wide persistent increased travel times could last for weeks after the flood water has receded, even after modest flood failure. A modest flooding of 1.3% road segments caused 8% temporal expansion of the entire traffic network. The results also show that distant trips would experience a greater percentage increase in travel time. Also, the extent of the increase in travel time does not decay with distance from inundated areas, suggesting that the spatial reach of flood impacts extends beyond flooded areas. The findings of this study provide an important novel understanding of floods’ impacts on the functioning of traffic networks in terms of travel time and traffic network geometry.

Reducing Red Light Running (RLR) with Adaptive Signal Control: A Case Study

Traffic accidents are a leading cause of premature death for citizens, with millions of injuries and fatalities occurring annually. Due to the fact that a large proportion of accidents are caused by red light running, reduction of the frequency of red light running (RLR) has been extensively researched in recent years. However, most of the previous studies have focused on reducing RLR frequency through driver education or warning sign design, with little attention paid to the relationship between RLR behavior and traffic signal control. Considering RLR is significantly affected by the number of vehicles arriving during yellow, it is possible to identify RLR behaviors in advance by analyzing data on yellow-arriving vehicles. Meanwhile, based on the strong correlation between yellow arriving and RLR frequency, it is possible to reduce RLR by traffic signal control. In this paper, we propose a quantitative model of correlation between RLR frequency and yellow light arrival based on high-resolution traffic and signal event data from Twin Cities, Minnesota. On this basis, the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is implemented to find trade-offs between minimizing the RLR frequency and the traffic delay. A case study of a 6-intersection arterial road reveals that in unsaturation, saturation, and supersaturation flow, our approach can converge to a Pareto optimal front in 30–50 iterations, which shows that is possible to simultaneously reduce RLR frequency and enhance traffic efficiency safety, which is conducive to ensuring the life safety of traffic participants.

Using Multidimensional Data to Analyze Freeway Real-Time Traffic Crash Precursors Based on XGBoost-SHAP Algorithm

The traditional freeway safety studies with “poststatic” thinking basically use cross-sectional data or panel data, which find it difficult to figure out real-time traffic crash risk factors. With the development of information collection technology, it is possible to obtain high-resolution traffic flow data currently, which provide a data basis for the dynamic traffic safety research towards freeways. This research aims at accurately identifying the real-time traffic crash precursors on freeways and addressing the shortcomings of conventional dynamic traffic safety research with the thinking of limited factor dimensions. In this research, dimensional data were applied as input model variables, the input dataset includes traffic crash data and the matched dynamic traffic flow data, and weather information and road characteristics were also considered to figure out the interaction effects between these dimensional factors. The XGBoost (eXtreme Gradient Boosting) was carried out to identify the dynamic crash-prone variables and the SHAP (SHapley Additive exPlanations) interpreter was introduced to interpret the XGBoost model, as well as the visualization of the influence of each eigenvalue on the traffic crash was realized. The results indicate that, in addition to traffic flow variables, road, weather, and temporal characteristics also have an impact on the traffic crash risk, and there is an interaction between each feature. The results of this research can provide the theoretical basis for freeway real-time traffic crash prediction and safety control.

Reliable Traffic State Identification Using High-Resolution Data: A Consistent Offline-Online Dynamic Time Warping–Based Time Series Clustering Approach

Reliable real-time traffic state identification (TSI) provides key support for traffic management and control. Although substantial efforts have been devoted to TSI, considering the high dynamics and stochasticity of traffic flows, there remain challenges in providing reliable and consistent TSI results, especially in online network–level applications. In this study, we propose a time series clustering-based offline-online modeling framework for reliable TSI using high-resolution traffic data. Specifically, in the proposed framework, the offline module extracts representative traffic state patterns from massive historical data, which serve as the state references in the online module when performing real-time TSI with streaming information. Instead of point data, the proposed framework uses high-resolution traffic data in the form of time series, providing rich information on traffic flows and details on their short-term fluctuations and stable long-term trends. In the offline module, considering the fuzziness of traffic states, we introduce a fuzzy c-means based clustering method for offline traffic flow series clustering and traffic state pattern extraction, within which the dynamic time warping algorithm is adopted for measuring the similarity between different time series, and the optimal number of clusters is determined by a proposed critical segment–based method to reach consistent TSI in network-wide applications. In the online module, a dynamic programming–based real-time TSI approach is developed to produce reliable and smooth identification results. Extensive numerical experiments on a 20-mi-long freeway corridor in California, USA, were performed to validate the proposed framework. Results demonstrate the effectiveness of the proposed framework.

Green time usage metrics on signalized intersections and arterials using high-resolution traffic data

The performance of traffic signal phasing and timing (SPaT) plans are directly related to temporal fluctuations in traffic volumes and their distribution across competing movements at the intersection. A well-timed signal plan generally allocates green times in proportion to the observed volumes. With the advance of data collection techniques and technology, it is possible to obtain and use high-resolution traffic data using detectors at intersections to measure the real-time performance of the SPaT plan to better achieve this goal. This study introduces novel green time metrics and methods to evaluate the efficiency of green time allocation along arterials and at individual intersections using automated, real-time, and high-resolution traffic data. An empirical application is presented for four intersections along a major radial arterial in Salt Lake City, Utah and a detailed analysis of the phases and movements for a single intersection located on that arterial. The proposed methods and results can help identify opportunities to improve the signal timing plan for the entire corridor or movements within secondary approaches by identifying locations and/or time periods where signal timing does not align well with observed traffic volumes. These metrics can be automated to provide traffic engineers with an alert as to when a particular intersection should be examined as a candidate for signal retiming, and they can also help identify what changes need to be made with the current SPaT plans.

Smart flood resilience: harnessing community-scale big data for predictive flood risk monitoring, rapid impact assessment, and situational awareness

Smart resilience is the beneficial result of the collision course of the fields of data science and urban resilience to flooding. The objective of this study is to propose and demonstrate a smart flood resilience framework that leverages heterogeneous community-scale big data and infrastructure sensor data to enhance predictive risk monitoring and situational awareness. The smart flood resilience framework focuses on four core capabilities that could be augmented by the use of heterogeneous community-scale big data and analytics techniques: (1) predictive flood risk mapping; (2) automated rapid impact assessment; (3) predictive infrastructure failure prediction and monitoring; and (4) smart situational awareness capabilities. We demonstrate the components of these core capabilities of the smart flood resilience framework in the context of the 2017 Hurricane Harvey in Harris County, Texas. First, we present the use of flood sensors for the prediction of floodwater overflow in channel networks and inundation of co-located road networks. Second, we discuss the use of social media and machine learning techniques for assessing the impacts of floods on communities and sensing emotion signals to examine societal impacts. Third, we describe the use of high-resolution traffic data in network-theoretic models for nowcasting of flood propagation on road networks and the disrupted access to critical facilities, such as hospitals. Fourth, we introduce how location-based and credit card transaction data were used in spatial analyses to proactively evaluate the recovery of communities and the impacts of floods on businesses. These analyses show that the significance of core capabilities of the smart flood resilience framework in helping emergency managers, city planners, public officials, responders, and volunteers to better cope with the impacts of catastrophic flooding events.

Optimizing Moving Object Trajectories from Roadside Lidar Data by Joint Detection and Tracking

High-resolution traffic data, comprising trajectories of individual road users, are of great importance to the development of Intelligent Transportation Systems (ITS), in which they can be used for traffic microsimulations and applications such as connected vehicles. Roadside laser scanning systems are increasingly being used for tracking on-road objects, for which tracking-by-detection is the widely acknowledged method; however, this method is sensitive to misdetections, resulting in shortened and discontinuous object trajectories. To address this, a Joint Detection And Tracking (JDAT) scheme, which runs detection and tracking in parallel, is proposed to mitigate miss-detections at the vehicle detection stage. Road users are first separated by moving point semantic segmentation and then instance clustering. Afterwards, two procedures, object detection and object tracking, are conducted in parallel. In object detection, PointVoxel-RCNN (PV-RCNN) is employed to detect vehicles and pedestrians from the extracted moving points. In object tracking, a tracker utilizing the Unscented Kalman Filter (UKF) and Joint Probabilistic Data Association Filter (JPDAF) is used to obtain the trajectories of all moving objects. The identities of the trajectories are determined from the results of object detection by using only a certain number of representatives for each trajectory. The developed scheme has been validated at three urban study sites using two different lidar sensors. Compared with a tracking-by-detection method, the average range of object trajectories has been increased by >20%. The approach can also successfully maintain continuity of the trajectories by bridging gaps caused by miss-detections.

Modest flooding can trigger catastrophic road network collapse due to compound failure

Compound failures occur when urban flooding coincides with traffic congestion, and their impact on network connectivity is poorly understood. Firstly, either three-dimensional road networks or the traffic on the roads has been considered, but not both. Secondly, we lack network science frameworks to consider compound failures in infrastructure networks. Here we present a network-theory-based framework that bridges this gap by considering compound structural, functional, and topological failures. We analyze high-resolution traffic data using network percolation theory to study the response of the transportation network in Harris County, Texas, US to Hurricane Harvey in 2017. We find that 2.2% of flood-induced compound failure may lead to a reduction in the size of the largest cluster where network connectivity exists, the giant component, 17.7%. We conclude that indirect effects, such as changes in traffic patterns, must be accounted for when assessing the impacts of flooding on transportation network connectivity and functioning.

Identification of dynamic traffic crash risk for cross-area freeways based on statistical and machine learning methods

Freeway traffic safety should be given great attention due to the frequent and serious consequences that arise from freeway traffic crashes. With the possibility of obtaining high resolution traffic big data, traditional freeway safety promotion methods are gradually replaced by the emerging technology of active safety control based on the real-time traffic data. However, there is still a lack of cross-area pertinence towards dynamic traffic safety currently. To overcome the defects of existing dynamic traffic crash precursors identification studies and provide precise theoretical basis for freeway dynamic safety control, this research took cross-area freeways as the research object. Firstly, based on the LOS (level of service) A–F theory and the consideration of area types, six units to be evaluated were generated (two dimensions totally: saturated/unsaturated flow, urban/suburban/mountainous). Then, conditional logistic regression based on Markov chain Monte Carlo method was adopted to quantitatively evaluate the dynamic risk of cross-area freeways. Finally, 20 related traffic flow variables were extracted from five dimensions which can reflect the dynamic traffic flow characteristics, and the machine learning approaches including random forest algorithm and Bayesian logistics regression were applied for analysis and modeling; then, crash precursors were identified for each type of freeway area, and the statistical relationship between traffic flow variables and crash risk was established via the statistical models. The results show the area types and traffic conditions of freeway are significantly correlated with dynamic traffic safety, and the crash risk in the urban area/saturated flow condition is the highest, which is 29.6 times of that in the condition of suburban area/unsaturated flow. When the traffic operates at the freeway of urban/unsaturated flow, the formation of small fleets and the lane changing behavior play a dominant role influencing crash risk; when operating at urban/saturated flow, frequent acceleration or deceleration in the fleet and frequent lane changes during the formation of the fleet have great impact towards crash risk. When traffic operates at suburban/unsaturated flow, the main crash precursors are the sudden change of occupancy and volume in a short period, as well as the formation of a small fleet. When operating at suburban/saturated flow, the crash-prone variables are the sudden increase of speed and occupancy in a short period and the change of volume. When traffic operates at mountainous/unsaturated flow, the changes of speed and occupancy in a short period and the formation of small fleets are the main factors influencing crash. When operating at mountainous/saturated flow, lane changing behavior, the sudden increase of volume, and the speed mutation of some vehicles in saturated flow are the significant precursors. The results indicate every area type of freeway has a different mechanism of traffic crash. Moreover, with the consideration of freeway area types and traffic state differences, the machine learning and statistical combination models proposed in this research can identify the relationship and mechanism between dynamic traffic flow characteristics and traffic safety more comprehensively and accurately, and the identification results in this research can refer to the further dynamic crash prediction work for cross-area freeway.

Real-Time Pedestrian Conflict Prediction Model at the Signal Cycle Level Using Machine Learning Models

Compared with traditional traffic studies, real-time safety analyses can be better incorporated into proactive traffic management strategies to improve traffic safety. However, few studies have investigated the real-time pedestrian safety model. Intersections usually have mixed traffic conditions with more pedestrian-vehicle interactions. This paper uses conflict indicators, PET (Post Encroachment Time) and TTC (Time to Collision) to identify pedestrians’ conflicts from CCTV (closed-circuit television) videos. The high-resolution traffic data from the Automated Traffic Signal Performance Measures (ATSPM) system are used to derive traffic flow-related variables. The pedestrian exposure is also estimated. Pedestrians’ conflicts are predicted using multiple machine learning models and Logistic Regression. The resampling methods, random over-sampling, and random under-sampling are compared. The best model, Extreme Gradient Boosting (XGBT) with random over-sampling method can achieve AUC (area under the ROC curve) value of 0.841 and recall value of 0.739 on the test data set. The proposed model can predict pedestrians’ conflicts one cycle ahead, which can be 2–3 min. The proposed model has the potential to be implemented in the Connected and Automated Vehicles (CAV) environment to adjust signal timing accordingly and enhance traffic safety.

Nonlinear Traffic Prediction as a Matrix Completion Problem with Ensemble Learning

This paper addresses the problem of short-term traffic prediction for signalized traffic operations management. Specifically, we focus on predicting sensor states in high-resolution (second-by-second). This contrasts with traditional traffic forecasting problems, which have focused on predicting aggregated traffic variables, typically over intervals that are no shorter than five minutes. Our contributions can be summarized as offering three insights: first, we show how the prediction problem can be modeled as a matrix completion problem. Second, we use a block-coordinate descent algorithm and demonstrate that the algorithm converges in sublinear time to a block coordinate-wise optimizer. This allows us to capitalize on the “bigness” of high-resolution data in a computationally feasible way. Third, we develop an ensemble learning (or adaptive boosting) approach to reduce the training error to within any arbitrary error threshold. The latter uses past days so that the boosting can be interpreted as capturing periodic patterns in the data. The performance of the proposed method is analyzed theoretically and tested empirically using both simulated data and a real-world high-resolution traffic data set from Abu Dhabi, United Arab Emirates. Our experimental results show that the proposed method outperforms other state-of-the-art algorithms.

Use of Real-Time Traffic and Signal Timing Data in Modeling Occupant Injury Severity at Signalized Intersections

This study explored the use of real-time traffic events and signal timing data to determine the factors influencing the injury severity of vehicle occupants at intersections. The analysis was based on 3 years (2017–2019) of crash and high-resolution traffic data. The best fit regression was first identified by comparing the conventional regression model and logistic models with random effect. The logistic model with a heavy-tailed distribution random effect best fitted the data set, and it was used in the variable assessment. The model results revealed that about 13.6% of the unobserved heterogeneity comes from site-specific variations, which underlines the need to use the logistic model with a random effect. Among the real-time traffic events and signal-based variables, approach delay and platoon ratio significantly influenced the injury severity of vehicle occupants at 90% Bayesian credible interval. Additionally, the manner of a collision, occupant seat position, number of vehicles involved in a crash, gender, age, lighting condition, and day of the week significantly affected the vehicle occupant injury. The study findings are anticipated to provide valuable insights to transportation agencies for developing countermeasures to mitigate the crash severity risk proactively.

Automatic Vehicle Tracking with LiDAR-Enhanced Roadside Infrastructure

Abstract Vehicle tracking technology is a prerequisite for the connected-vehicle (CV) system. However, a mixture of CV and unconnected vehicles will be under normal conditions on roads in the near future. How to obtain the real-time traffic status of unconnected vehicles remains a challenge for traffic engineers. The roadside Light Detection and Ranging (LiDAR) sensor provides a solution for collecting real-time high-resolution micro traffic data of all road users (CV and unconnected vehicles). This article developed a systematic procedure for vehicle tracking using the roadside LiDAR sensors. The procedure can be divided into five major parts: point registration, background filtering, point clustering, object classification, and vehicle tracking. For each step, the corresponding data processing algorithms were provided. A field test was conducted to evaluate the performance of the proposed method. Compared to the state-of-the-art method, the proposed methods can track vehicles with higher accuracy and lower computation loads.

Evaluation of displaced left-turn intersections

Displaced left-turn intersections (DLTs) are designed to enhance the operational performance of conventional intersections that are congested due to heavy left-turn traffic volumes by excluding the left-turn movements at the main intersection. This results in reducing the number of potential conflict points and increasing the intersection capacity. However, since drivers are not familiar with DLTs’ operation, there is a need to assess the safety and operational efficiency of this type of intersections. This paper evaluates the safety performance of DLTs using two common methods, which are a before-and-after study with comparison group and cross-sectional analysis. Furthermore, it investigates the operational performance of DLTs using a general linear model describing the relationship between a selected measure of performance and other operational and geometric characteristics based on high-resolution traffic data. The safety analysis indicates that DLTs can increase crash frequency in comparison to conventional intersections. In addition, the operational analysis implies that DLTs have the potential to reduce the delay at intersections. Consequently, evaluation of the different aspects of costs and benefits were conducted to decide if this alternative design is appropriate for implementation. The study concludes that DLTs have negative safety impacts in comparison to conventional intersections for many crash types, but it might be more efficient for operational performance. It is recommended that appropriate safety countermeasures should be developed and implemented to enhance traffic safety at DLTs.

An automatic lane identification method for the roadside light detection and ranging sensor

The roadside Light Detection and Ranging (LiDAR) sensor can provide the high-resolution micro traffic data (HRMTD) of all road users by collecting real-time point clouds in three-dimensional (3D) space. The HRMTD collected by the roadside LiDAR provides a solution to fill the data gap under the mixed situation (both connected vehicles and unconnected vehicles exist on the roads) for connected vehicle technologies. Lane identification is important information in HRMTD. The current lane identification algorithms are mainly developed for autonomous vehicles, which could not be directly used to process roadside LiDAR data. This article provides an innovative algorithm to automatically identify traffic lanes for the roadside LiDAR data. The proposed lane identification algorithm includes five major steps: background filtering, point clustering, object classification, frame aggregation, and traversal search. The parameters used in the algorithm are selected by balancing the time cost and the accuracy. With the GPS information, the location of the lane can be transferred into the GoogleEarth and be compared with the location of the lane in real world. The testing results showed that the average distance error (ADE) compared to the real location in Google Earth was less than 0.1 m. This robust lane identification can release engineers from the manual lane identification task and avoid any error caused by manual work. The extracted lane locations can be used for researchers and practitioners to locate the vehicles precisely in different applications.

Gramian-Based Optimization for the Analysis and Control of Traffic Networks

This paper proposes a simplified version of classical models for urban traffic networks and studies the problem of optimizing the network overall efficiency by controlling the signalized intersections. Differently from traditional approaches to control traffic signaling, the tractability of our framework allows us to effectively model large-scale interconnections and enables the design of critical parameters while considering network-wide measures of efficiency. Motivated by the increasing availability of real-time high-resolution traffic data, we cast an optimization problem that formalizes the goal of optimizing vehicle evacuation by controlling the durations of green lights at the intersections under the current congestion conditions. Our framework allows us to relate efficiency objectives with the optimization of a metric of controllability of the associated dynamical network. We then provide a technique to efficiently solve the optimization by parallelizing the computation among a group of distributed agents. Last, we assess the benefits of the proposed modeling and optimization framework through macroscopic and microscopic simulations on daily commute scenarios for the urban interconnection of Manhattan, NY, USA.