Meead Saberi

Exploring Properties of Networkwide Flow-Density Relations in a Freeway Network

The objective of this study is to investigate the properties of network-level traffic flow relationships in a freeway network with the use of commonly available loop detector data. The impact of the spatial and temporal distribution of congestion in a selected network on the shape and properties of the flow–density relation is investigated, with emphasis on the formation and characterization of hysteresis patterns. Accordingly, a path-dependent characterization of hysteresis patterns in freeway networks is introduced and illustrated conceptually as well as through empirical observations. Comparison of the spatial and temporal distribution of congestion throughout a selected subnetwork on different days suggests a relationship between the size of the hysteresis loop and the inhomogeneity of the traffic distribution. The maximum network average flow is not a constant value but varies across different days. In addition, for the same value of average network occupancy, the variation of occupancy is higher during the recovery period compared with the loading period. The observed large variation in network occupancy during recovery implies the formation of fragmented queues and traffic instability. A chaotic pattern is also to be expected in the networkwide flow–occupancy plane when the spatial distribution of link densities is inhomogeneous and the average network occupancy remains consistently high and roughly unchanged for successive time intervals. Overall, the study results provide a deeper understanding of the properties of networkwide relations on freeway networks.

Hysteresis and Capacity Drop Phenomena in Freeway Networks: Empirical Characterization and Interpretation

The objective of this study was to characterize hysteresis and capacity drop phenomena in freeway networks with the use of commonly available loop detector data from three networks: Chicago, Illinois; Portland, Oregon; and Irvine, California. For exploration of the effects of variations in network topology and size on the network fundamental diagram, a comparison was made by using the observed flow–occupancy diagrams of the selected freeway networks. The results provide further confirmation that findings from the literature for a limited number of networks are also valid for freeway networks not previously studied. Freeway networks have been found more likely to exhibit an inconsistent hysteretic pattern in shape and size that depends on the spatial distribution of congestion over the network. On the basis of empirical observations, hysteresis loops were characterized by their shape and size. Two shapes of hysteresis loops, H1 and H2, were identified and characterized. The size of each hysteresis loop was believed to be characterized by its width, height, and the area covered by the hysteresis loop. The authors postulated that the capacity drop phenomenon existed in freeway networks in a manner similar to that in individual freeway sections. Two types of capacity drop were identified. Type 1 was associated with the inability of the freeway network to sustain its throughput at its peak value for a relatively long time, and therefore, capacity dropped while demand was still high and the network was loading. Type 2 was associated with the instability of network traffic when the network underwent reloading (e.g., afternoon peak period) after an incomplete recovery from the initial loading (e.g., morning peak period). In some cases, this reloading resulted in a lower capacity in the afternoon than in the morning. Empirical results showed that the observed phenomena were reproducible on different days and for different networks.

Macroscopic modeling of pedestrian and bicycle crashes: A cross-comparison of estimation methods

The paper presents a cross-comparison of different estimation methods to model pedestrian and bicycle crashes. The study contributes to macro level safety studies by providing further methodological and empirical evidence on the various factors that influence the frequency of pedestrian and bicycle crashes at the planning level. Random parameter negative binomial (RPNB) models are estimated to explore the effects of various planning factors associated with total, serious injury and minor injury crashes while accounting for unobserved heterogeneity. Results of the RPNB models were compared with the results of a non-spatial negative binomial (NB) model and a Poisson-Gamma-CAR model. Key findings are, (1) the RPNB model performed best with the lowest mean absolute deviation, mean squared predicted error and Akaiki information criterion measures and (2) signs of estimated parameters are consistent if these variables are significant in models with the same response variables. We found that vehicle kilometers traveled (VKT), population, percentage of commuters cycling or walking to work, and percentage of households without motor vehicles have a significant and positive correlation with the number of pedestrian and bicycle crashes. Mixed land use is also found to have a positive association with the number of pedestrian and bicycle crashes. Results have planning and policy implications aimed at encouraging the use of sustainable modes of transportation while ensuring the safety of pedestrians and cyclist.

Connecting Networkwide Travel Time Reliability and the Network Fundamental Diagram of Traffic Flow

The existence of the network fundamental diagram (NFD) has been established at the urban network scale. It relates three traffic descriptors: speed, density, and flow. However, its deterministic nature does not convey the underlying variability within the network. In contrast, travel time reliability as a network performance descriptor is of growing concern to both the traveling public and traffic managers and policy makers. The objectives of this paper were to extend travel time reliability modeling from the link–path level to the network level and to connect overall network variability to NFD. Robust relationships between travel time variability and network density and flow rate were analytically derived, investigated, and validated with both simulated and real-world trajectory data. The distance-weighted standard deviation of travel time rate, as a measure of travel time variability, was found to increase monotonically with network density. A maximum network flow rate existed beyond which network travel time reliability deteriorated at a much faster pace. The results also suggest that these relationships are inherent network properties (signature) that are independent of demand level. The effects of en route information on the proposed relationships were also studied. The results showed that en route information reduced network travel time variability. The findings provide a strong connection between NFD and travel time variability, and this connection can be used further for modeling of network travel time reliability and assessment of measures intended to improve reliability of travel in a network.

Calibration of traffic flow models under adverse weather and application in mesoscopic network simulation

The weather-sensitive traffic estimation and prediction system (TrEPS) aims for accurate estimation and prediction of the traffic states under inclement weather conditions. Successful application of weather-sensitive TrEPS requires detailed calibration of weather effects on the traffic flow model. In this study, systematic procedures for the entire calibration process were developed, from data collection through model parameter estimation to model validation. After the development of the procedures, a dual-regime modified Greenshields model and weather adjustment factors were calibrated for four metropolitan areas across the United States (Irvine, California; Chicago, Illinois; Salt Lake City, Utah; and Baltimore, Maryland) by using freeway loop detector traffic data and weather data from automated surface-observing systems stations. Observations showed that visibility and precipitation (rain–snow) intensity have significant impacts on the value of some parameters of the traffic flow models, such as free-flow speed and maximum flow rate, while these impacts can be included in weather adjustment factors. The calibrated models were used as input in a weather-integrated simulation system for dynamic traffic assignment. The results show that the calibrated models are capable of capturing the weather effects on traffic flow more realistically than TrEPS without weather integration.

Estimating Network Fundamental Diagram Using Three-Dimensional Vehicle Trajectories: Extending Edie’s Definitions of Traffic Flow Variables to Networks

This paper evaluates measurement methods for traffic flow variables taken at the network level. Generalized Edies definitions of fundamental traffic flow variables along highways are extended for considering vehicles traveling in networks. These definitions are used to characterize traffic flow in networks and form the basis for estimating relationships among network density, flow, and speed in the form of a network fundamental diagram. The method relies on three-dimensional vehicle trajectories to provide estimates of network flow, density, and speed. Such trajectories may be routinely obtained from particle-based microscopic and mesoscopic simulation models and are increasingly available from tracking devices on vehicles. Numerical results from the simulation of two networks, in Chicago, Illinois, and Salt Lake City, Utah, are presented to illustrate and validate the estimation methodology. As part of the verification process, the study confirms that the traffic flow fundamental identity (Q = K · V) holds at the network level only when networkwide traffic flow variables are defined consistently with Edies definitions.

Implementation and Evaluation of Weather-Responsive Traffic Management Strategies

This study presents the development and application of methodologies to support weather-responsive traffic management (WRTM) strategies by building on traffic estimation and prediction system models. First, a systematic framework for implementing and evaluating WRTM strategies under severe weather conditions is developed. This framework includes activities for planning, preparing, and deploying WRTM strategies in three different time frames: long-term strategic planning, short-term tactical planning, and real-time traffic management center operations. Next, the evaluation of various strategies is demonstrated with locally calibrated network simulation-assignment model capabilities, and special-purpose key performance indicators are introduced. Three types of WRTM strategies [demand management, advisory and control variable message signs (VMSs), and incident management VMSs] are applied to multiple major U.S. areas, namely, Chicago, Illinois; Salt Lake City, Utah; and the Long Island area in New York. The analysis results illustrate the benefits of WRTM under inclement weather conditions and emphasize the importance of incorporating a predictive capability into selecting and deploying WRTM strategies.

Continuous Approximation Model for the Vehicle Routing Problem for Emissions Minimization at the Strategic Level

AbstractThis paper presents a continuous approximation model for the vehicle routing problem for emissions minimization (EVRP) and demonstrates the model’s applicability and usefulness with numerical studies based on implementations of the Solomon test instances. The EVRP is a variant of the time-dependent vehicle routing problem (TDVRP) in which minimizing emissions is an additional objective of the model. The continuous approximation model presented in this paper will facilitate strategic planning of one-to-many distribution systems and evaluate the effects of emissions costs. Furthermore, results from the continuous model can provide guidelines for constructing solutions for the discrete EVRP. Results from a sensitivity analysis indicated that the optimal number of dispatches during the peak period is smaller than the optimal number of dispatches during the off-peak period when considering the temporal effects of congestion. Results revealed that the potential cost savings attributable to incorporating...

Definition and Properties of Alternative Bus Service Reliability Measures at the Stop Level

The Transit Capacity and Quality of Service Manual (TCQSM) provides transit agencies with tools for measuring their systems performance in different levels of operation. Bus service reliability as one of the key performance measures has become a major concern of both transit operators and users because it significantly affects users’ experience and service quality perceptions. The objective of this paper is to assess the existing reliability measures proposed by TCQSM and develop new reliability measures at the stop level. Utilizing empirical data from archived Bus Dispatch System (BDS) data in Portland, Oregon, a number of key characteristics of distributions of delay and headway deviation are identified and three new reliability measures at the stop level are proposed. The results of this study can be implemented in transit operations for use in improving schedules and operations strategies. Also transit agencies can use the proposed reliability measures in order to evaluate and prioritize stops for operational improvement purposes.

Pedestrian Crowd Dynamics Observed at Merging Sections: Impact of Designs on Movement Efficiency

The need for reliable crowd simulation tools has necessitated an accurate understanding of human behavior and the rules that govern their movements under normal and emergency escapes. This paper investigates the dynamics of merging streams of pedestrians. In the merging sections, the interaction between pedestrians and geometric features of merging sections can significantly impede the collective motion and can increase the possibility of flow breakdown, particularly under emergency conditions. Therefore, to create safe and efficient designs, it is important to study human movement characteristics associated with these types of conflicting geometries. In this study, empirical data collected from large numbers of high-density experiments with people at different desired speed levels were used to explore the effect of different merging configurations (i.e., design and angle) on dynamics of merging crowds. For the first time, this study examined the impact of elevated speed regimes (as a behavioral proxy of emergency escapes) on the movement efficiency of crowds in merging sections with different geometric designs. In particular, this study investigated the impact of these conflicting geometric settings on the average waiting time in the system as a measure of movement efficiency. Results suggest that the experienced delay is dramatically greater in asymmetrical setups compared with the delay in symmetrical setups and that the difference is even more pronounced at elevated levels of pedestrians’ desired speed. These findings give significant insights into the implications of inefficient designs of merging sections for pedestrians’ safety, notably when quick movement of crowds is necessary (e.g., in emergencies).