This study proposes a methodology to optimize truck arrival patterns to reduce emissions from idling truck engines at marine container terminals. A bi-objective model is developed minimizing both truck waiting times and truck arrival pattern change. The truck waiting time is estimated via a queueing network. Based on the waiting time, truck idling emissions are estimated. The proposed methodology is evaluated with a case study, where truck arrival rates vary over time. We propose a Genetic Algorithm based heuristic to solve the resulting problem. Result shows that, a small shift of truck arrivals can significantly reduce truck emissions, especially at the gate.
Interested in data visualization and urban problems? Why not go to the launch of the Visualizing Urban Data Idea Lab on April 30th at 5:00 PM in the Blum Hall Student Collaborative Space. There will be free pizza!
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.
In this paper we aim to contribute to the literature on the empirical parallels between urban hierarchies and the transport networks supporting and/or reflecting these hierarchies. We adopt a stochastic actor-based modeling framework to analyze the co-evolution of the world city hierarchy and global air passenger networks between 2000 and 2010/2011. The data are drawn from an inventory of the location strategies of globalized service firms across world cities and the International Civil Aviation Organization (ICAO). Major findings include (1) exogenous effects, such as the impact of economic development and regionality; (2) endogenous micro-level effects producing macro-level patterns, such as preferential attachment processes; and (3) the two-way impact of both networks. (i.e., cities that are well connected in the aviation network tend to attract more major offices of globalized service firms, while the co-presence of major offices of globalized service firms in cities in turn stimulates the development of aviation connections between them).
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.
This paper investigates the factors that affect travel behaviour within neighbourhoods in Tyne and Wear, North East England while accounting for differences in attitudes and perceptions. Ten different neighbourhoods have been carefully selected to characterise the two different types of traditional and suburban neighbourhood street layouts. A self-administered questionnaire has been delivered to 2200 households to capture neighbourhood design, travel patterns, travel attitudes and socio-economic characteristics. Multivariate analysis of cross-sectional data shows that some socio-economic variables as well as travel attitudes and neighbourhood design preferences can explain the differences in travel patterns between the two distinct neighbourhood designs. The results show additionally that the traditional neighbourhood group is more sensitive to factors of perception and attitudes in relation to neighbourhood design that lead to walking, cycling and public transport use travel patterns, suggesting that land-use policy designed to accommodate lower carbon-based travel together with measures to encourage active travel will have greater impact on the traditional group than the suburban group. This finding suggests that generic measures imposed by many governments, and certainly implied by current UK land-use policy, to promote sustainable mobility should be selectively targeted.
Next week is Spring Break for UC Berkeley and the ITS Library will be closed. Lots of students will be going on vacation because it's that time in the semester when you just want to get away. Student travel behavior is often mythic - the trips to the beach - but is that reality? What about different market segments?
Whatever you do, be safe. We'll see you on Monday April 1.
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.