Knowledge-workers and the sustainable city: the travel consequences of car-related job-perks

Attracting firms in knowledge and technology intensive (KTI) sectors is highly desired at both the national and the regional level as a powerful engine of economic growth. Due to fierce competition in KTI sectors and national taxation policies, KTI firms often attract high-quality employees by offering car-related job perks as additional incentives to wage. In Canada, car allowance is offered by 46% of the employers to attract highly-skilled workers. In Israel, 61% of knowledge-workers in the KTI sectors receive a company-car with respect to 16% of workers in other sectors. In the U.K., car-related job perks are offered by 18% of the employers. This study focuses on the impact of car-related job-perks on the travel behavior of knowledge-workers. The importance of this issue derives from the impact of the travel behavior of knowledge-workers on congested transportation networks in metropolitan areas, as knowledge-based economy tends to concentrate mainly in metropolitan regions. This study applies discrete choice models in order to analyze the impact of company-cars and car allowances (reimbursement of fuel and parking expenses) on commute and leisure travel of knowledge-workers. The analyzed data consist of 750 observations, retrieved from a revealed-preferences survey among KTI workers who work and reside in the Tel-Aviv metropolitan area in Israel. Results show that car-related job perks are associated with (i) high annual mileage, (ii) high propensity of using the car as main commute mode, (ii) long commute distances and travel times, (iii) high trip chaining frequency in commuting trips, and (iii) high frequency of long-distance weekend leisure trips. Result also show that KTI workers generally prefer the car or non-motorized transport modes over the bus system. These results suggest that the development of sustainable knowledge-based cities should consider (i) the replacement of car-related job perks by other incentives, (ii) the provision of pedestrian and cyclist friendly infrastructures, and (iii) public transport improvements.

Nonrandom Mixing Models of HIV Transmission

Models of HIV transmission and the AIDS epidemic generally assume random mixing among those infected with HIV and those who are not. For sexually transmitted HIV, this implies that individuals select sex partners without regard to attributes such as familiarity, attractiveness, or risk of infection. This paper formulates a model for examining the impact of nonrandom mixing on HIV transmission. We present threshold conditions that determine when HIV epidemics can occur within the framework of this model. Nonrandom mixing is introduced by assuming that sexually active individuals select sex partners to minimize the risk of infection. In addition to variability in risky sex rates, some versions of our model allow for error (or noise) in information exchanged between prospective partners. We investigate several models including random partner selection (or proportionate mixing), segregation of the population by risky sex rates, a probabilistic combination of segregation and random selection induced by imperfect information (or preferred mixing), and a model of costly search with perfect information. We develop examples which show that nonrandom mixing can lead to epidemics that are more severe or less severe than random mixing. For reasonable parameter choices describing the AIDS epidemic, however, the results suggest that random mixing models overstate the number of HIV infections that will occur.AIDs; Random Mixing Models; Search Costs

Estimating Time and Size of Bioterror Attack

Time and size of possible bioterror event estimated in real time

A Heavy Traffic Approximation for Queues with Restricted Customer-Server Matchings

We consider a queuing system with n customer classes and m servers. For each class i there is only a subset S(i) of servers that are able to process customer' i requests and they do that using a first-come-first-serve discipline. For this system, we are primarily interested in computing Pij , the steady-state fraction of class-i customers that are served by server j. We also look at stability conditions and standard performance measures like waiting times and queue lengths. Under the assumption that the system is heavy loaded, we approximate Pij as well as the other performance measures. Computational experiments are used to show the quality of our approximations.Operations Management Working Papers Serie