Monday, August 19, 2013

Hot or Not? by Janson & Levinson

HOT or Not?
Driver Elasticity to Price on the MnPASS HOT Lanes

Michael Janson and David Levinson

"The Minnesota Department of Transportation (MnDOT) has added MnPASS High Occupancy Toll (HOT) lanes on two freeway corridors. While not the fi rst HOT lanes in the country, the MnPASS lanes are the fi rst implementation of road pricing in Minnesota and possess a dynamic pricing schedule. Tolls charged to single occupancy vehicles (SOVs) are adjusted every three minutes according to HOT lane vehicle density. Given the infancy of systems like MnPASS, questions remain about drivers' responses to toll prices. Three fi eld experiments were conducted on the corridors during which prices were changed. Data from the fi eld experiments as well as two years of toll and traffi c data were analyzed to measure driver responses to pricing changes. Driver elasticity to price was positive with magnitudes less than 1.0. This positive relationship between price and demand is in contrast with the previously held belief that raising the price would discourage demand. In addition, drivers consistently paid between approximately $60-120 per hour of travel time savings, much higher than the average value of time. Reasoning for these results is discussed as well as the implications these results have on the pricing of HOT lanes."

Also see the following article on The Atlantic Cities:
HOT Lanes Are Even More Popular When They're Expensive

Thursday, August 15, 2013

Travel Behavior, Self-formed Urban Communities, and Sociodemographic Characteristics

Following is the racial dot map of Chicago. 1 dot = 1 person


Following is a map of the identified community clusters based on urban trips reported in the 2007 Household Travel Survey data of Chicago using a modularity maximization algorithm (courtesy of Dirk Brockmann). Modularity is often used in optimization methods for detecting community structure in networks.


Qualitatively observing, there is somewhat a correlation between the two maps. It would be interesting to quantitatively explore that how sociodemographic factors affect the travel-based community structure in the Chicago region: understanding the relationship between travel behavior, community formation, and sociodemographic characteristics.

Sunday, August 11, 2013

Smart Growth and Urban Goods Movement

See the NCFRP report here: http://onlinepubs.trb.org/onlinepubs/ncfrp/ncfrp_rpt_024.pdf
"TRB’s National Cooperative Freight Research Program (NCFRP) Report 24: Smart Growth and Urban Goods Movement identifies the interrelationships between goods movement and smart growth applications, in particular, the relationship between the transportation of goods in the urban environment and land-use patterns."

Tuesday, August 6, 2013

Transportation Journals Ranking

Following ranking includes SJR 4-yr and 2-yr (2011) impact factor and Google Scholar H5 index (2008-2012)

Integration of Land Use and Transportation Modeling in Ohio

The Ohio Department of Transportation released a report on integration of land use and travel behavior modeling in the state of Ohio:
"Facing major challenges related to energy consumption, global warming, environmental quality, and economic viability, metropolitan regions around the world are examining the consequences of alternative growth patterns on resource consumption. As we plan for new land use policies and investments in the transportation system over the next decade, we will face a new set of challenges tied to the changing demographic and economic conditions in Ohio, in addition to the rising costs of energy and related policies aimed at reducing the carbon footprint of our economy. The first step in understanding the possible implications of these changes is a deeper understanding of the current relationships between land use and travel behavior, and how these might be impacted by future land use, transportation and energy policies."

Friday, August 2, 2013

Bipartite Networks: Application in Transit Networks

A bipartite network is a kind of network in which the nodes are grouped into two independent classes of U and V and every link connects a node in U to a node in V.

A transit network can be viewed as a bipartite network such that group U consists of different geographical locations in the city (origins and destinations) and V consists of different transit lines or routes. A node in U (e.g. an origin) can only connect to another node in U (e.g. a destination) if and only if a transit line/route exists that connects them. Therefore, an origin will be connected to a destination (both nodes in U group) via a transit line/route (a node in V group). Such network representation illustrates a transit network with no transfer on-route. Note that transfers are possible in this representation but only through reaching an intermediate destination in U.


The bipartite network can be modified to represent transit networks with one or more transfers. The modified bipartite network includes two independent groups of nodes (U and V) in which every link connects a node in U to a node in V. Also, there are links that connects nodes in V, representing transfers between different transit routes/lines. Thus, an origin in U can also be connected to a destination, also in U, via two or more transit lines/routes.

The discussed application of bipartite networks in transit networks and the proposed modification to illustrate transit networks with transfers is an ongoing work and not yet published. No reproduction is allowed without written permission.


Following is an example of the bipartite and modified partite of Chicago transit network.