Reliability of travel time in traffic networks is affected by a variety of factors,some external (e.g. demand surges, weather) and others inherent to the behavior of the traffic stream, reflecting complex dynamics among interacting agents. Yet remarkably simple collective effects emerge when examining the relation between the standard deviation of the trip time per unit distance to the corresponding mean at the network level. We examine this relation for several networks using both simulated and actual data from vehicle probes. We connect this variance to other traffic variables defined at the network level, providing a simple characterization of travel time reliability as a function of density. We consider within-day and day-to-day variability and propose a compound gamma model to capture overall variation. To evaluate the reliability implications of different transportation options and operational strategies using simulation tools, a scenario-based approach is proposed and demonstrated.
The seminar takes place this Friday, February 21, 2014 from 4-5 PM in 534 Davis. Cookie Hour commences at 3:30 in the library.
Traffic crashes and accidents at intersections, roundabouts and roadway segments result from many complex factors, but at a basic level, they are outcomes of the interactions among vehicles and other road users. Since few direct measurements of these interactions are available, engineers and planners instead attempt to understand them by studying crashes and accidents reports. As crashes account for a tiny fraction of safety conflicts, these reports fail to provide a full understanding of what is happening at the points of accidents. This is especially true of crashes involving pedestrians and bicycles, for which data are sparse, making it difficult to determine reliable patterns. In this talk we will present risk based traffic safety models using multiple data streams, including near miss data, systemic data, historical traffic accidents, and drivers’ naturalistic behavior data. We will also briefly discuss ongoing research at Rutgers on the development of Plan4Saefty software, which is currently being used by the State of New Jersey for traffic safety analysis and planning.
The seminar will be held Friday, February 7 2014 from 4:00-5:00 PM in 534 Davis Hall. Cookie Hour commences at 3:30 here in the library.
In the spirit of Open Access Week, here's an interesting article from an open access journal - The Journal of Transport and Land Use. Go check it out and peruse the articles. No need to depend on your institution's sibscription because it's free to the public! (Thanks open access!)
This paper presents a methodology to investigate the link between bicycle activity and built environment, road and transit network characteristics, and bicycle facilities while also accounting for spatial autocorrelation between intersections. The methodology includes the normalization of manual cyclist counts to average seasonal daily volumes (ASDV), taking into account temporal variations and using hourly, daily, and monthly expansion factors obtained from automatic bicycle count data. To correct for weather conditions, two approaches were used. In the first approach, a relative weather ridership model was generated using the automatic bicycle count and weather data. In the second approach, weather variables were introduced directly into the model. For each approach, the effects of built environment, road and transit characteristics, and bicycle facilities on cyclist volumes were determined. It was found that employment, schools, metro stations, bus stops, parks, land mix, mean income, bicycle facility type (bicycle lanes and cycle tracks), length of bicycle facilities, average street length, and presence of parking entrances were associated with bicycle activity. From these, it was found that the main factors associated with bicycle activity were land-use mix, cycle track presence, and employment density. For instance, intersections with cycle tracks have on average 61 percent more cyclists than intersections without. An increase of 10 percent in land-use mix or employment density would cause an increase of 8 percent or 5.3 percent, respectively, in bicycle flows. The methods and results proposed in this research are helpful for planning bicycle facilities and analyzing cyclist safety. Limitations and future work are discussed at the end of this paper.
This week's TRANSOC Friday Seminar is at a special time - 11:00am - noon in 534 Davis. This week Northwestern University Associate Professor Yu (Marco) Nie will present on a cap-and-trade approach to congesiton management in "From Pricing to Cap-and-Trade: Analysis and Design of Quantity-based Approach to Congestion Management."
Traffic congestion continues to threaten economic prosperity and quality of life around the world. It is widely acknowledged that demand management is an indispensable ingredient in the recipe for solving the traffic congestion puzzle, and likely to be one of the more effective and cost-efficient if properly implemented. This research will explore a new and promising travel demand management strategy, inspired by various cap-and-trade schemes aiming to reduce greenhouse gas and air pollutant emissions. The cap-and-trade schemes considered in this research seek to couple direct travel demand restriction with a trading mechanism. Because such a scheme typically involves creating mobility credits and trading them in a market, it is also known as tradable credit scheme. In this talk we will examine a few key design issues involved in building such credit markets, including how to account for the effects of transaction cost and how to initially allocate credits, using various analytical models.
As noted above, the seminar is happening this Friday, October 25 2013, from 11:00am to noon in 534 Davis. We'll keep you posted about Cookie Hour.
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.
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.
Access Across America, a study by David Levinson, the R.P. Braun/CTS Chair in Transportation Engineering at the University of Minnesota, goes beyond congestion rankings to focus on accessibility: a measure that examines both land use and the transportation system. The study is the first systematic comparison of trends in accessibility to jobs by car within the U.S. By comparing accessibility to jobs by automobile during the morning peak period for 51 metropolitan areas, the study tells us which cities are performing well in terms of accessibility and which have seen the greatest change.
The full report can be found here. And here's the data!California is well represented with Los Angeles (1), SF-Oakland (2), and San Jose (6) all in the Top Ten.
About 8.1 percent of U.S. workers have commutes of 60 minutes or longer, 4.3 percent work from home, and nearly 600,000 full-time workers had "megacommutes" of at least 90 minutes and 50 miles. The average one-way daily commute for workers across the country is 25.5 minutes, and one in four commuters leave their county to work.