Vinayak Dixit

Using Mobile Probe Data and the Macroscopic Fundamental Diagram to Estimate Network Densities: Tests Using Microsimulation

Recent advances in urban traffic network modeling have led to the proposal of several large-scale control strategies aimed at improving network efficiency, including metering vehicle entry, pricing network use, and allocating limited street space between multiple modes. However, these strategies typically require accurate real-time predictions of networkwide traffic conditions to be implemented, and it is often taken for granted that this information is available. In practice, this is not a trivial issue, because measuring traffic conditions across a large urban network in real time is not straightforward. For that purpose, this paper presents a method of indirectly estimating average vehicle densities across a network in real time by combining travel speed information from a few circulating probe vehicles with the macroscopic fundamental diagram (MFD) of urban traffic. The proposed method is advantageous because it requires relatively little data and involves few calculations. Tests of this methodology on a simulated network showed that the results were not accurate when the network was uncongested, but reliable density estimates could be obtained when the network was congested or approaching congestion, even if only a small fraction of vehicles served as probes. This result is promising because congested states are the most critical. Therefore, this methodology seems useful as a traffic-monitoring scheme to complement networkwide control strategies, provided that the network exhibits a well-defined and reproducible MFD.

Assessment of I-4 Contraflow Plans: Microscopic Versus Mesoscopic Simulation

Better use of the available road network is critical to improving the evacuation operation during a disaster. Contraflow operations help increase the capacity of the available network by reversing the direction of inbound lanes to outbound lanes. This helps improve the outflow from a region threatened by disaster. One of the major issues associated with contraflow operations is determining the locations for access to the contraflow lanes from the normal-flow lanes. These accesses are also referred to as crossovers. Four different strategies with different crossover locations were tested on the I-4 evacuation route from Tampa to Orlando, Florida. It was found that the provision of two crossovers, one after Tampa and another after Plant City, performed the best but was only marginally better than the provision of one crossover after Tampa. Therefore, considering the cost and personnel needed to provide a crossover, the provision of one crossover after Tampa was found to be a more logical choice than the provision of two crossovers. It was observed that the time required to run the microscopic simulation to arrive at the results was extremely long. To overcome this drawback, the cell transmission model (CTM) was calibrated and run for the same four strategies. It was observed that the results were extremely close to the results from the microscopic simulation. The robustness and speed of CTM make it ideal for use as part of a decision support system to help determine the best strategies in real time. This will help emergency management officials make real-time decisions in the event of unforeseen drops in capacities because of incidents or vehicle breakdowns.

Modeling risk attitudes in evacuation departure choices

The decision of whether and when to evacuate can be characterized as decision making under risk. Presently, most models assume linear utility functions through which it is impossible to disentangle factors that influence risk attitudes and other factors that affect decision making under risk. There is a need to disentangle and study factors that affect risk attitudes from factors that affect an evacuees preparation time. The aim in doing so is to provide planners and practitioners with an ability to measure a persons risk attitude and develop appropriate strategies that could motivate people to evacuate. This study is expected to connect the theory of risk developed in economic theory with behavior under threat. The paper uses the Hurricane Andrew response data in conjunction with time-dependent data on the probability of a hurricane strike and the category of the hurricane data to develop a model for evacuation departure choice. A constant relative risk aversion specification is used to model risk attitudes. The process of an evacuation is abstracted as an individual being given a choice between two lotteries: either to stay or leave. The results show that the model is able to predict the total number of evacuees and the time varying evacuation rates with reasonable accuracy. Factors such as time of day, length of time spent in a region, and whether a mandatory evacuation order was issued affected risk attitudes. The presence of children affected the amount of time spent preparing if the family decided to stay.

HURRICANE EVACUATION: ORIGIN, ROUTE AND DESTINATION

Evacuation operations can be divided into three main levels: at the origin (region at risk), routes, and destination. This research encompasses all the three aspects and proposes a framework to assess the whole system in its entirety. At the origin the demand dictates when to schedule evacuation orders, it also dictates the capacity required on different routes. On the routes it is crucial to determine where to provide access to the contraflow lanes. This research also provides a new and innovative technique to improve operations by operating a “network breathing strategy” at destinations. These breakthroughs will provide a framework for a real time decision support system.

Understanding the Impact of a Recent Hurricane on Mobilization Time During a Subsequent Hurricane

It is not uncommon for a region to be affected by multiple hurricanes in a span of a few weeks. The behavior of the evacuees during a subsequent hurricane in the same season is affected by the damage to the infrastructure and to the vehicles and assets belonging to evacuees, as well as by the psychological impact of the preceding hurricane. One such behavioral aspect that affects traffic-loading rates during a hurricane is the evacuation delay or mobilization time. In this study, “mobilization time for an evacuee” is defined as the difference between the time at which the decision to leave is made and the actual time of departure. This paper proposes a methodology that can be used to understand the factors associated with the mobilization time during a subsequent hurricane while accounting for the effects of the preceding hurricane. The effects of the preceding hurricane were accounted for by modeling mobilization times simultaneously with an ordinal variable representing evacuation participation levels during Hurricane Charley. The data from a survey conducted with the evacuees of Hurricane Frances, which made landfall 3 weeks after Hurricane Charley, were used in this study. The errors for the two simultaneously estimated models were significantly correlated. The results showed that home ownership, the number of individuals in the household, income levels, and the level or the risk of a surge were significant in the model and explained the mobilization times for households. Pet ownership and the number of children in households, known to increase mobilization times during isolated hurricanes, were not found to be significant in the model. The implications of these findings for the demand S-curve are briefly discussed.

Validation Techniques for Region-Level Microscopic Mass Evacuation Traffic Simulations

The experiences of several recent evacuations have demonstrated how a mass evacuation of a major city can affect traffic throughout an entire region. This realization has brought the need for analyzing and evaluating evacuation plans at a regional level. Numerous recent studies have devoted themselves to the topic of simulating large-scale evacuations. However, few studies have developed procedures for the validation of large-scale models. This paper discusses validation within the context of the recent development of the regional multimodal evacuation model for New Orleans, Louisiana. The New Orleans model is unique because it is among the first ever to incorporate qualitative and quantitative model validation procedures based on field data collected during an actual mass evacuation. The paper discusses the various statistics considered for validation, including their inherent advantages and disadvantages. It also presents the results obtained from the validation exercises of the New Orleans model. The study concluded that regression analyses were the most appropriate for statistically analyzing the spatial and temporal data correlations between the traffic patterns produced within the simulation and those actually observed during the Hurricane Katrina evacuation. From a qualitative standpoint, colorized spatiotemporal maps were also found to be quite effective for visualizing traffic speed and volume patterns. The maps were also invaluable for quickly identifying and analyzing bottleneck areas at both the local and regional levels.

Autonomous Vehicles: Disengagements, Accidents and Reaction Times

Autonomous vehicles are being viewed with scepticism in their ability to improve safety and the driving experience. A critical issue with automated driving at this stage of its development is that it is not yet reliable and safe. When automated driving fails, or is limited, the autonomous mode disengages and the drivers are expected to resume manual driving. For this transition to occur safely, it is imperative that drivers react in an appropriate and timely manner. Recent data released from the California trials provide compelling insights into the current factors influencing disengagements of autonomous mode. Here we show that the number of accidents observed has a significantly high correlation with the autonomous miles travelled. The reaction times to take control of the vehicle in the event of a disengagement was found to have a stable distribution across different companies at 0.83 seconds on average. However, there were differences observed in reaction times based on the type of disengagements, type of roadway and autonomous miles travelled. Lack of trust caused by the exposure to automated disengagements was found to increase the likelihood to take control of the vehicle manually. Further, with increased vehicle miles travelled the reaction times were found to increase, which suggests an increased level of trust with more vehicle miles travelled. We believe that this research would provide insurers, planners, traffic management officials and engineers fundamental insights into trust and reaction times that would help them design and engineer their systems.

Relationship between mean and day-to-day variation in travel time in urban networks

The day-to-day reliability of transportation facilities significantly affects travel behavior. To better understand how travelers use these facilities, it is critical to understand and characterize this reliability for different facilities. Early work in this area assumed that the variance of day-to-day travel times (a measure of the inverse of reliability) increases proportionally with the mean travel time; i.e., as the mean travel time increases, travel time reliability decreases. However, recent empirical data for a single bottleneck facility and a small urban network suggest a more complex relationship that exhibits hysteresis. When this phenomenon is present, the variance in travel time is larger as the mean travel time decreases (congestion recovery) than as the mean travel time increases (congestion onset). This paper presents an elegant theoretical model to describe the variance of travel times across many days in an urban network. This formulation shows that the hysteresis behavior observed in empirical floating car data on urban networks should not be unexpected, and that it is linked to the hysteresis loops that often exist in the Macroscopic Fundamental Diagram of urban traffic. To verify the validity of this formulation, data from a micro-simulation of the City of Orlando, Florida, are used to derive an observed relationship with which to compare to theory. The simulated data are shown to match the theoretical predictions very well, and confirm the existence of hysteresis in the relationship between the mean and variance of travel times that is suggested by theory. These results can be used as a first step to more accurately represent travel time reliability in future models of traveler decision-making.

Modeling Origin-Destination Effects on Roundabout Operations and Inflow Control

Traffic flows around roundabouts have been found to be dependent on origin-destination flows, but the true nature of this relationship is not properly understood. Present analyses are based on either gap acceptance models or empirical models. These models do not properly account for the impact of origin-destination flows on roundabout operations. This has limited the possibility to develop strategies that improve roundabout operations by controlling inflows. This research proposes a theory to analyze roundabout traffic flows and a strategy to determine inflows into a roundabout that would maximize the outflow from the roundabout. This strategy could be implemented through use of signals to meter vehicles at the entry. To achieve this, a theoretical framework is proposed based on the macroscopic fundamental diagram for urban networks. The theory and strategy is then tested using microscopic simulation. It was found that the outflow from a roundabout is dependent on the average flow and the average trip length around the roundabout. The average trip length is a function of the origin-destination flows.

Integrating the Bus Vehicle Class Into the Cell Transmission Model

The traditional cell transmission model (CTM), a well-known dynamic traffic simulation method, does not cater to the presence of moving bottlenecks, which may be caused by buses traveling within a network. This may affect the dynamics of congestion that is present and may also affect route choice by all vehicles on a network. The main contribution of this paper is to provide an analytical formulation for a mixed traffic system that includes cars and buses, which realistically replicates moving bottlenecks. We modify the CTM model using methods from the lagged CTM to recognize speed differentials between the free-flow speed of buses and cars. In addition, the impact of capacity reduction caused by buses was incorporated. These developments led to the replication of moving bottlenecks caused by buses within the CTM framework. The formulated variant of CTM was utilized to determine a system optimal assignment that minimizes the total passenger travel time across cars and buses. The proposed modified CTM model, defined as the BUS-CTM, has been applied on a road link and a more detailed network to demonstrate the effectiveness of the approach. The numerical results and the depiction of the bottleneck phenomenon within the model suggests that the BUS-CTM obtains more realistic results compared with the application of the traditional CTM in a mixed car-bus transportation system. The sensitivity analysis shows that bus passenger demand, passenger occupancy of bus, and bus free-flow speeds are the key parameters that influence the system performance.