Springer Science and Business Media LLC

On average, temporary jobs are far less stable than permanent jobs. This higher instability could potentially lower workers’ incentives to relocate towards the workplace, thereby resulting in longer commutes. However, surprisingly few studies have investigated the link between temporary employment and commuting length. Building on the notion that individuals strive to optimize their utility when deciding where to work and live, we develop and test a theoretical framework that predicts commuting outcomes for different types of temporary workers – fixed-term, casual and temporary agency workers – and in different institutional contexts. We estimate fixed-effects regression models using 17 waves of data from the Household, Income and Labour Dynamics in Australia (HILDA) Survey and the German Socio-Economic Panel (SOEP). As expected, the results show that the link between temporary employment and commuting length varies by employment type and institutional context. Agency work is associated with longer commutes than permanent work in both countries, whereas this applies to fixed-term contracts for Germany only. For casual work, the findings suggest no commuting length differential to permanent employment. In terms of policy, our findings suggest lengthy commuting can be a side effect of flexible labour markets, with potentially negative implications for worker well-being, transportation management and the environment.

This study proposes a novel Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. The strength of this method is that it is possible to uncover the VTT distribution (and its moments) without making assumptions about the shape of the distribution or the error terms, while being able to incorporate covariates and taking the panel nature of stated choice data into account. To assess how well the proposed ANN-based method works in terms of being able to recover the VTT distribution, we first conduct a series of Monte Carlo experiments. After having demonstrated that the method works on Monte Carlo data, we apply the method to data from the 2009 Norwegian VTT study. Finally, we extensively cross-validate our method by comparing it with a series of state-of-the-art discrete choice models and nonparametric methods. Based on the promising results we have obtained, we believe that there is a place for ANN-based methods in future VTT studies.

The U.S. Department of Transportation (1974) recently completed a comprehensive study of mass transportation needs and methods for financing these needs. Using information from the 1974 National Transportation Study, the study determined the level of capital and operating funds that would be required to implement the 1972–90 long-range plans and 1972–80 short-range programs of the states and urbanized areas. It then analyzed various funding mechanisms at state and local levels for financing their portions of these plans and programs. It was found that urban areas, in general, not only plan to stabilize transit fares in the face of rising costs, but also intend to put $ 23.6 billion into capital investments through 1980 and an additional $ 34.6 billion through 1990. Of the total $ 58.2 billion in capital expenditures by 1990, 63% would be expended by the nine largest urbanized areas; 27.8% by the New York area alone. Rail transit and commuter railroad costs would account for 90% of the nine largest urbanized areas. States and localities would be able to carry the financial burden of mass transportation improvements, even if the proposed 1980 programs were implemented in their entirety, given current levels of Federal assistance. However, there would have to be a substantial financial commitment from the states and localities and some hard decisions made by them about public expenditure priorities, fare policies, and taxation levels, and policies to discourage automobile usage. This underscores the need for careful review of their overall plans and programs by state and local officials before making financial commitments.

The advent of express coach bus lines offering guaranteed seating and emphasizing curbside pickup and drop-off is contributing to a revival in intercity bus travel in the United States. Relatively little is known, however, about the scale and geographic scope of these carriers or the competitive landscape in which they operate. To fill this void, this study evaluates the service networks operated by the two largest express coach operators in the country, BoltBus and Megabus, and evaluates a data set of 4775 fares sold on megabus.com. The results show that these carriers cumulatively serve 127 intercity segments and operate about 52.9 million bus miles per year. Together, these carriers have grown to about one third of the size of Amtrak, with Megabus and BoltBus providing 3.3 billion and .69 billion seat miles of service, respectively, compared to Amtrak’s 12.8 billion. With respect to the types of routes it serves and the competition it faces, Megabus has evolved into a carrier quite different than Boltbus; more than one third of Megabus’ bus miles are operated on segments without Amtrak service, while virtually all of BoltBus’ miles face this competition. The analysis of Megabus’ pricing shows that fares rise modestly within 2 weeks of departure, while the per-mile costs are much less ($.08/mile) for 300–399 mile trips than for those 50–99 miles ($.22/mile). Nevertheless, the dispersion of fares tends to fall as the departure date nears, regardless of distance. Together, these prominent bus lines serve 66 of the 100 most heavily traveled U.S. city pairs that have characteristics suitable for intercity bus service—which is more than Amtrak. With further growth on the horizon, planners, federal regulators, and researchers should collaborate on establishing reporting requirements for this expanding sector.