Recent research has studied the existence and the properties of a macroscopic fundamental diagram (MFD) for large urban networks. The MFD should not be universally expected as high scatter or hysteresis might appear for some type of networks, like heterogeneous networks or freeways. In this paper, we investigate if aggregated relationships can describe the performance of urban bi-modal networks with buses and cars sharing the same road infrastructure and identify how this performance is influenced by the interactions between modes and the effect of bus stops. Based on simulation data, we develop a three-dimensional vehicle MFD (3D-vMFD) relating the accumulation of cars and buses, and the total circulating vehicle flow in the network. This relation experiences low scatter and can be approximated by an exponential-family function. We also propose a parsimonious model to estimate a three-dimensional passenger MFD (3D-pMFD), which provides a different perspective of the flow characteristics in bi-modal networks, by considering that buses carry more passengers. We also show that a constant Bus–Car Unit (BCU) equivalent value cannot describe the influence of buses in the system as congestion develops. We then integrate a partitioning algorithm to cluster the network into a small number of regions with similar mode composition and level of congestion. Our results show that partitioning unveils important traffic properties of flow heterogeneity in the studied network. Interactions between buses and cars are different in the partitioned regions due to higher density of buses. Building on these results, various traffic management strategies in bi-modal multi-region urban networks can then be integrated, such as redistribution of urban space among different modes, perimeter signal control with preferential treatment of buses and bus priority.
Had the report based estimates on more current historic data—e.g., VMT trends for 2003-13, which grew at one-fifth the USDOT’s 1995-2010 estimate—the cost estimates would have dropped by tens of billions more, reducing pressure on budgets while freeing up funds to bring the existing system to a state of good repair.
Freeway analysis procedures in the widely used Highway Capacity Manual (HCM) include the input of a driver population factor (Fp), which allows the analyst to adjust the demand depending on the familiarity of drivers with the roadway. This adjustment is based on the assumption that unfamiliar drivers will drive at slower speeds with longer headways and that higher capacity would therefore be required. However, little research supports the use of the Fp, and the HCM cautions against the use of Fp unless the analyst is fairly certain the traffic stream is actually unfamiliar with the roadway. As an experiment, three bottlenecks in California were selected and analyzed during the weekday peaks and weekend afternoons in periods during which the traffic stream was likely to be nonlocal. The results showed that the changes in flow were minor at all three locations. Further research with additional sites and an increased awareness of the definition of familiarity will be required to confirm the results from this research.
Road safety is a significant concern to a broad range of stakeholders. Various approaches and strategies have been used to enhance road safety across the world. In this regard, the 4 Es used to characterize safety initiatives are Engineering, Education, Enforcement, and Emergency medicine. The developed nations have adopted a more comprehensive approach to incorporate the 4 Es, while the less developed nations focus primarily on engineering initiatives. The seminar will highlight some of the strategies adopted in Las Vegas, Nevada and across Iowa in the United States. These will be complemented with comments about challenges involved in improving overall road safety in Kerala, India. Further, examples of effective non-engineering based strategies will be presented. The seminar will also touch upon lessons learned from these experiences, and key considerations that are important for sustainable success of campaigns to enhance road safety.
The seminar will take place Friday, February 28, 2014 in 534 Davis from 4-5 PM. TRANSOC Cookie Hour will be in the library at 3:30.
Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples’ physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance.
Dynamic traffic routing refers to the process of (re)directing vehicles at junctions in a traffic network according to the evolving traffic conditions. The traffic management center can determine desired routes for drivers in order to optimize the performance of the traffic network by dynamic traffic routing. However, a traffic network may have thousands of links and nodes, resulting in a large-scale and computationally complex non-linear, non-convex optimization problem. To solve this problem, Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. ACO is implemented online to determine the control signal – i.e., the splitting rates at each node. However, using standard ACO for traffic routing is characterized by four main disadvantages: 1. traffic flows for different origins and destinations cannot be distinguished; 2. all ants may converge to one route, causing congestion; 3. constraints cannot be taken into account; and 4. neither can dynamic link costs. These problems are addressed by adopting a novel ACO algorithm with stench pheromone and with colored ants, called Ant Colony Routing (ACR). Using the stench pheromone, the ACR algorithm can distribute the vehicles over the traffic network with less or no traffic congestion, as well as reduce the number of vehicles near some sensitive zones, such as hospitals and schools. With colored ants, the traffic flows for multiple origins and destinations can be represented. The proposed approach is also implemented in a simulation-based case study in the Walcheren area, the Netherlands, illustrating the effectiveness of the approach.
This study explores the limiting properties of network-wide traffic flow relations under heavily congested conditions in a large-scale complex urban street network; these limiting conditions are emulated in the context of dynamic traffic assignment (DTA) experiments on an actual large network. The primary objectives are to characterize gridlock and understand its dynamics. This study addresses a gap in the literature with regard to the existence of exit flow and recovery period. The one-dimensional theoretical Network Fundamental Diagram (NFD) only represents steady-state behavior and holds only when the inputs change slowly in time and traffic is distributed homogenously in space. Also, it does not describe the hysteretic behavior of the network traffic when a gridlock forms or when network recovers. Thus, a model is proposed to reproduce hysteresis and gridlock when homogeneity and steady-state conditions do not hold. It is conjectured that the network average flow can be approximated as a non-linear function of network average density and variation in link densities. The proposed model is calibrated for the Chicago Central Business District (CBD) network. We also show that complex urban networks with multiple route choices, similar to the idealized network tested previously in the literature, tend to jam at a range of densities that are smaller than the theoretical average network jam density. Also it is demonstrated that networks tend to gridlock in many different ways with different configurations. This study examines how mobility of urban street networks could be improved by managing vehicle accumulation and redistributing network traffic via strategies such as demand management and disseminating real-time traveler information (adaptive driving). This study thus defines and explores some key characteristics and dynamics of urban street network gridlocks including gridlock formation, propagation, recovery, size, etc.
This research explored how two states of affect, emotion and mood, would influence driver’s risky driving behavior through risk perception and risk attitude. An experiment and a survey were adopted to test the two paths. In this model, negative affect played an opposite and more powerful role compared to positive affect. Study 1 was an experimental study with four treatment groups. Participants watched one of four video clips (traffic-related negative, traffic-unrelated negative, positive and neutral) and different emotions were induced. Negative emotion significantly elevated drivers’ risk perception but such perception failed to develop an appropriate attitude for drivers. A more favorable risk attitude resulted in increased reports of speeding. Turning from a “point” effect to a similar “period” effect, a survey was carried out in Study 2 to explore the effect of positive and negative mood instead of emotion. Mood states affected drivers’ risky driving behavior through risk perception as well as risk attitude, which was in line with the results of Study 1. The “bad is stronger than good” effect and the two paths in the model were discussed.
After decades of study, the value of travel time remains incompletely understood and ripe for further theoretical and empirical investigation. Research has revealed many regularities and connections between willingness to pay for time savings and other economic factors including time of day choice, aversion to unreliability, labor supply, taxation, activity scheduling, intra-household time allocation, and out-of-office productivity. Some of these connections have been addressed through sophisticated modeling, revealing a plethora of reasons for heterogeneity in value of time rooted in behavior at a micro scale. This paper reviews what we know and what we need to know. A recurrent theme is that the value of time for a particular travel movement depends strongly on very specific factors, and that understanding how these factors work will provide new insights into travel behavior and into more general economic choices.