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.
On the basis of real traffic and environmental data measured on German freeways, we studied common features of traffic congestion under the influence of severe weather conditions. We have found that traffic features [J] and [S] defining traffic phases “wide moving jam” (J) and “synchronized flow” (S) in Kerner's three-phase theory are indeed common spatiotemporal traffic features. The quantitative parameters for both traffic phases [S] and [J] were investigated in a comparison of “ideal” weather conditions (good visibility and no precipitation) and severe weather situations (icy road, wind, precipitation, etc.). We showed spatiotemporal congested patterns in several space–time diagrams based on the Automatic Tracking of Moving Jams/Forecasting of Traffic Objects (ASDA/FOTO) model reconstruction for roadside detectors. A statistical study of traffic phase [J] parameters was presented, showing the average values and standard deviation of the quantities. Similarities and differences were analyzed, and some consequences for vehicular applications were discussed to cope with severe weather conditions.
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.
The paper then considers both the morning and evening peaks together for a single mode bottleneck (all cars) with identical travelers that share the same wished times. For a schedule penalty function of the morning departure and evening arrival times that is positive definite and has certain properties, a user equilibrium is shown to exist in which commuters travel in the same order in both peaks. The result is used to illustrate the user equilibrium for two cases: (i) commuters have decoupled schedule preferences in the morning and evening and (ii) commuters must work a fixed shift length but have flexibility when to start. Finally, a special case is considered with cars and transit: commuters have the same wished order in the morning and evening peaks. Commuters must use the same mode in both directions, and the complete user equilibrium solution reveals the number of commuters using cars and transit and the period in the middle of each rush when transit is used.
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.
We find that the major usage of each road segment can be traced to its own - surprisingly few - driver sources. Based on this finding we propose a network of road usage by defining a bipartite network framework, demonstrating that in contrast to traditional approaches, which define road importance solely by topological measures, the role of a road segment depends on both: its betweeness and its degree in the road usage network. Moreover, our ability to pinpoint the few driver sources contributing to the major traffic flow allows us to create a strategy that achieves a significant reduction of the travel time across the entire road system, compared to a benchmark approach.
Drivers from Sanjose, Hayward, Dublin, San Rafel and San Ramon often find themselves stuck in the worst traffic. Could more metering be the answer?