Urban road congestion, capacity expansion and port competition: empirical analysis of US container ports

Abstract

In this study, we empirically investigate the impacts of urban road congestion and road capacity expansion on the competition between major container ports in the US. We find that more delays on urban roads may cause shippers to switch to competing rival ports: a 1% increase in road congestion delays around the port is associated with a 0.90–2.48% decrease in the port's container throughput but a 0.62–1.69% increase in the rival port's throughput. Adding local roads tends to benefit the port and harm its rival (in terms of throughput) by reducing road congestion. However, the overall impact of road provision on ports’ throughput varies among the sample ports, as road capacity expansion may affect ports’ output through channels other than road congestion delays.

Acknowledgments

We appreciate the constructive comments and suggestions from two anonymous referees and the guest editor, Meifeng Luo. We also thank Garland Chow and conference participants in the 1st International Workshop on Port Economics, Singapore, December 2011, and the 1st International Conference of the Association of Transport, Trade and Service Studies, Hong Kong, June 2012, for helpful discussions. Financial support from the Social Science and Humanities Research Council of Canada (SSHRC) is gratefully acknowledged.

Notes

1. TTI (2010) estimated that in 2009, the average (per urban area) congestion costs suffered by trucks in the US amounted to 1273 million US dollars and 215 million US dollars for very large (population over 3 million) and large (population over 1 million but less than 3 million) urban areas, respectively.

2. Other congestion mitigation strategies include: improving operational efficiency, providing truck dedicated facilities, improving traffic management, and managing demand (e.g., through congestion pricing). For more detailed discussion, see FHWA (2004) and Golob and Regan (2000).

3. Other theoretical studies in this strand of the literature include Yuen, Basso, and Zhang (2008) and Zhang (2008). The former investigates the effects of congestion pricing implemented at a gateway port on its hinterland's optimal road pricing, road congestion and social welfare, whereas the latter focuses on the corridor congestion and capacity investment rather than urban road congestion per se.

4. Pope et al. (1995) assessed the impact of adding a new section of highway on road congestion around the port of Hampton Roads by a simulation model, but port competition was excluded from their analysis.

5. For simplicity, we normalize the vehicle operating costs, such as fuel costs and road tolls, to be zero. This simplification does not change the main results of this model. See Wan and Zhang (2013) for the full version of this model.

6. Note that the superscript * denotes the equilibrium outcomes.

7. De Borger, Proost, and Van Dender (2008) argued that it is quite plausible to have port i’s charge increase in road capacity

8. De Borger, Proost, and Van Dender (2008) showed that , , , and

9. A consulting report for the port of Los Angeles and the port of Long Beach finds that an increase in truck transportation costs will affect containers shipped by off-dock rail, transloaded to rail or shipped by truck alone with distance more than 150 miles the most. Near-dock rail and trucking within the range of 50–150 miles will be mildly affected. The impact on short distance trucking (less than 50 miles) will be the least (Moffatt and Nichol and BST Associates 2007).

10. Here, catchment area is different from urban area around the port. Urban area around the port can be considered as the metropolitan area the port belongs to. Usually, a port's catchment area is much larger than the metropolitan area and may include metropolitan areas in vicinity states. For example, the urban area of the port of Oakland consists of the populated area within the San Francisco-Oakland metropolitan area while the catchment area extends to San Jose and even a few counties in Nevada. The exact definitions of urban area and catchment area for ports in our sample are given in Section 4.

11. By looking at the US ports, we can focus on the interaction between the port throughput and the road system. As the Jones Act limits feeder services between US ports, US ports have little transshipment and have a “strong inland orientation” (Notteboom and Rodrigue 2005), and thus their throughputs are more likely to be affected by port access conditions.

12. These ports might also compete with Canadian west coast ports, namely, Vancouver and Prince Rupert.

13. Unfortunately, this series of surveys is only conducted once every 4–5 years, and we have data for 1993, 1997, 2002, and 2007, but our data set has observations from 1982 to 2009. Therefore, we assign the fraction calculated from the 1993 survey to all periods in 1982 to 1993, the 1997 fraction to all periods in 1994 to 1997, the 2002 fraction to all periods in 1998 to 2002 and the 2007 fraction to the remaining years.

14. Congested travel, congested system, and the number of rush hours do not provide any information about the level of congestion faced by each traveler. Annual total delays and annual total congestion costs are affected by the population size. Cost per peak traveler is affected by the assumption of fuel costs and commuters’ value of time. The travel time index is the ratio of peak travel time and free-flow travel time that measures congestion on each trip and each mile of travel. DPPT, however, reflects both the per-mile congestion and the length of each trip.

15. Note that we only specify one rival per port based on the distance. It is possible that one port has multiple rivals which may or may not be included in the sample. For example, SeaTac may compete with both Portland and Vancouver.

16. We also fit the pure first-order models by removing all the second-order terms in Equation (5)(5). Assuming there is no higher-order effects, the coefficients of ln(LM) and ln(LMR) can be interpreted as, respectively, the own-road capacity and the rival-road capacity elasticities. We find that the coefficient of ln(LM) is negative and statistically significant, while the coefficient of ln(LMR) is negative but not statistically significant.