Feasibility of Using Rail towards Addressing Freight Truck Capacity

4082 words (16 pages) Essay in Transportation

08/02/20 Transportation Reference this

Disclaimer: This work has been submitted by a student. This is not an example of the work produced by our Essay Writing Service. You can view samples of our professional work here.

Any opinions, findings, conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of UK Essays.

The Feasibility of Using Rail towards Addressing Freight Truck Capacity:
Lessons Learned from the Alameda Corridor Project in Los Angeles

Los Angeles County, California houses the Port of Los Angeles (LA Port) and Port of Long Beach (LB Port). These two ports are neighbors, but are under different jurisdictions with the former under the City of Los Angeles and the latter under the City of Long Beach[1]. Nevertheless, together, it is referred to as the San Pedro Bay Port Complex (the Port). In the early 1980’s, there was an increasing concern of ground transportation modes not being able to handle the growing amount of freight transport from the Port. As indicted by Hicks (1991), “In 1989, the two ports combined handled a total of 138.8 million metric revenue tons of cargo. By the year 2020, cargo volumes are expected to more than double” (Hicks, 1991, p 230). As a result, the Alameda Corridor was created. This consists of a freight railroad corridor with a total distance of 20-miles which spans from the Port vicinity to the City of Vernon which is south of Downtown Los Angeles. The corridor is a result of combining four different branch rail-lines onto a single corridor which was previously the San Pedro Branch. This single branch provides a connection between the Port and the rest of the nation through the greater rail network[2].

In addition to the rail corridor, the volumes to and from the Port are also transported via trucks through the road network which includes I-110 and I-710[3]. In addition to these interstate highways, there is also the California State Route (SR-) 47 which also connects to the Port and connects to I-110. SR-47 has a segment that includes Alameda Street, up to SR-91, which parallels the Alameda Rail Corridor[4]. The Alameda Corridor is in between the I-110 and the I-710 which are both north-south interstate highway routes with the former being west of the Alameda Corridor and the latter to the east[5].

According to the Port of Long Beach, in 2018, this port moved 8,091,023 twenty-foot equivalent unit (TEU) (Port of Long Beach, n.d.). From the same year, the Port of Los Angeles had 9,500,000 TEUs (The Port of Los Angeles, 2019). Together, this amounts to 17,591,023 TEUs, and according to Southern California Association of Governments (2016), this figure is projected to increase to 36,000,000 TEUs in 2035. Of the containers that are transported to/from the Port, the majority consists of imports of which its destination can be local[6], which comprises about 22 to 29 percent, and discretionary, which are destinations outside of local, has about 71 to 78 percent (Southern California Association of Governments, 2016). Drawing from SCAG’s projection, the Port is faced with continuing increase in demand and according to Nelson (2018), attempts to alleviate capacity issues on the I-710 have been controversial due to concerns of negative environmental impacts as the corridor is nicknamed “the diesel death zone” and displacement due to lack of right of way. As an attempt to address this constraint, a politician has advocated for shifting freight onto the rail corridor (Nelson, 2018). To further appreciate this more recent issue, through review of literature was performed regarding what effect implementing the Alameda Corridor has had on mode shift. Additionally, there will be a look into how effective freight rail can be towards addressing freight truck highway capacity constraints. Recommendations for improving the ground transportation connecting to the Port is also discussed.

Literature Review. Prior to reviewing the outcomes of the Alameda Corridor project, a review of literature regarding theory of freight mode choice is presented to provide as a basis for the upcoming discussion. Winston (1981) criticized earlier transportation demand studies as deficient which relied on an aggregated analysis that consequently concealed the ability to determine behavioral discrepancies between shippers and receivers. Additionally, such approach also led to the inability to find the extent of instances at which freight modes compete. To determine what important indicators shippers and receivers considered for their mode choice decision, the author analyzed behavioral theory pertaining to distribution centers, as well as, applied a disaggregate model.

The previous author argued analyzing receivers and shippers separately due to uncertainty which does not prevent and does not necessitate competitive results. This then creates the inability to analyze the two stakeholders as the same. Additionally, the concept of utility was applied that reflects his/her behavior and it involves risk, stemming from uncertainties, and indicators such as cost. The previous author explained risk becoming a part of utility analysis due to the probability associated with modal characteristics that impacts profits. Then, “…the expected utility of the ith mode is influenced by the attributes of the mode, attributes of the commodity, and characteristics of the firm” (Winston, 1981, p 990). In the situations where the shipper decides the mode, the receiver’s well-being is also factored in thereby necessitating maximizing the joint expected utility.

From the use of the probit model and its output indicates that for all commodity groups, freight charges have negative signs indicating an increase in price reduces demand, albeit at different constant values. However, for quality of service there are discrepancies. When considering mean transit times, the types of related costs include, “…obsolescence costs, in-transit interest costs, and the interest and storage costs of holding inventories” (Winston, 1981, p 994). Obsolescence costs can occur due to greater mean transit times particularly with commodity groups that are more time sensitive and consequently incurs decreasing value as transit time increases. Examples include newspapers and produce which has a shorter lifespan and decreases in value as in-transit time increases. Mean transit times also contributes towards calculating the inventory amount which then defines the interest costs and storage costs. For example, some commodity groups have positive signs for mean transit times such as Stone, Clay and Glass Products & Primary and Fabricated Metals. In particular, these two groups have higher deviation from no effect due to quality of service, one and two respectively with 95% confidence level, indicating materials that have higher storage costs may benefit from an increase in transit time as the vehicles act as storage units. When considering in-transit time deviation and reliability, which is standard deviation divided by mean, the commodity groups that are more time sensitive such as agriculture and Paper Printing and Publishing have negative and positive signs, respectively. Based on the author’s analysis, generally mode shift happens more with change in price rather than quality of service. However, this is not the case for agricultural commodities where improving quality of service may result in more rail (Winston, 1981).

Wang, Ding, Liu, and Xie (2013) performed a case study assessing the magnitude and in what way freight attributes impacts freight mode choice within Maryland. Freight mode that were analyzed included truck and rail. The previous authors utilized the “…Binary probit and logit models” (Wang, Ding, Liu, & Xie, p 1239, 2013) and the purpose was an attempt at finding differences in modal behavior along with attempting to validate the types of behavior found in three areas within the state. When interpreting the indicators from the probit model, freight materials were listed based on most likely utilizing truck, “Nondurable manufacturing>Food>Lumber>Durable Manufacturing…” (Wang, Ding, Liu, & Xie, 2013, p 1245). When comparing across the three areas within the state, all saw the more a freight material has increase in value of time, the more likely truck will be utilized. The previous authors presented a limitation regarding the indicators, weight and value, which the sign was positive. Rather, the previous authors believed the opposite in which an increase in weight and value of shipments are more likely to be transported by rail (Wang, Ding, Liu, & Xie, 2013).

Nam (1997), analyzed the feasibility of utilizing, in particular cases, aggregate models for performing mode choice pertaining to freight commodity types. The previous author utilized the logit model and “…a pooled model over commodity groups” (Nam, p 223,1997). From the previous author’s model, elasticity analysis was performed as well. Freight rate for material type groups pertaining to “paper” and “basic metal”[7] had higher elasticity for rail indicating more influence from changes in rates. Nonetheless, when looking at the freight material types, this indictor had more influence on elasticity on the rail side. Regarding transit time, all freight material types had greater elasticity as lεl > 1 for rail and is higher than road. Additionally, “…the results of direct elasticities indicate that transit time exerts the greatest influence on the shippers’ mode choice response for all commodity groups for both modes…” (Nam, p 229, 1997). Table 6 on page 229 provided the elasticity figures which demonstrates transit time had the highest elasticity values when compared with other indicators within each of the freight material types (Nam, 1997).

 Samimi, Kawamura, and Mohammadian (2011) carried out a study analyzing the behavioral aspects of what leads to freight mode choice outcomes by using binary logit and binary probit models. For the theoretical behavioral basis, the authors used random utility maximization, but due to limited disaggregate data partially resulting from privacy of private companies, the authors collected their own data. This previous study of mode choice, specifically pertained to truck versus rail including intermodal, from the perspectives of “shippers, third party logistics providers (3PLs) or receivers” (Samimi, Kawamura & Mohammadian, 2011, p 859). The authors’ models consisted of the following variables of which all were at least at the 95% confidence level: “Distance”, “Weight”, “Truck-time”, “Rail-time”, “Truck-cost-index”, “Rail-cost-index”, and “Potential-intermodal” (Samimi, Kawamura & Mohammadian, 2011, p 862)[8].

For improved appreciation of the effects the indicators had on mode choice, the previous authors used marginal effect study on both of their models, and also determined that the logit model had greater statistical significance. Accordingly, the previous authors decided to continue analyzing the logit model. Results demonstrated that the greater the shipping distance, then the more probable rail would be selected as rail is initially more expensive and these costs are reduced with greater distance trips. Additionally, the greater the freight load, the more probable trip is taken with rail. With regards to Potential-intermodal, this involves companies that at least typically views intermodal as feasible choice have increased probability of choosing rail. The previous authors viewed this indicator as potentially expanding explanation as shippers may be unaware of their options in rail thereby not including these types of indicators may not provide greater accuracy. Travel time is a greater influence for truck according to the elasticity analysis, “…the effect of truck travel time is almost 20 times greater for the truck mode” (Samimi, Kawamura, & Mohammadian, 2011, p 864). Accordingly, the decisionmaker was more influenced by travel time when choosing truck instead of rail. However, the decisionmaker was more influenced by costs when considering rail.

Agarwal, Guiliano, and Redfearn (2004) delved into evaluating the outcome of the Alameda Corridor project, as well as, reviewing if this project can occur at another location. The project proponents viewed the project achieving “…significantly reduce traffic congestion, air and noise pollution at the ports and surrounding region, especially the 710 freeway” (Agarwal, Guiliano, Redfearn, 2004, p 17). Among the benefits purported by the Alameda Corridor Transportation Authority (ACTA) includes the reduced increase regarding trucks on highways that have origin or destination and abated standing trains have ameliorated the region’s air quality. To the contrary of these claims, prior to the previous authors’ assessment, there has not been corroborative analytical studies regarding the project’s benefits. When the amount of trips was measured, the mean was 38 daily trips in the corridor which was about a quarter of capacity usage indicating underutilization. Additionally, from the approximate 4 million TEUs that were moved from the Port to the national rail network, 25% used truck rather than the rail corridor even though the private rail entities had to pay a fee as if they used the rail. Additionally, this occurred even though travel time via train may be shorter and the previous authors stated a potential reason for this, “At the Alameda Corridor conference, it was noted that the lack of near-dock intermodal transfer facility made it more economical for BNSF to truck its containers to the Los Angeles rail yards” (Agarwal, Giuliano, Redfearn, p 24, 2004). Private rail and ACTA agreed on private entities pay a fee by the amount of shipping containers moved on the rail corridor, and this fee still applies if the entity uses trucks to the national rail network.

The previous authors delved into why the rail corridor only captured about 27% of freight from the Port. Trucks typically become the preferred mode when average distance is less and average value of commodity load is greater (Agarwal, Guiliano, Redfearn, 2004). The utilization of intermodal becomes feasible above the threshold of 500 miles[9]. According to Agarwal, Guiliano, and Redfearn (2004), greater than three-quarters of total truck tonnage pertain to distances less than 50 miles. About half to 60% of commodities from the Port have lower probability for utilizing the rail corridor as a quarter of the commodities have destinations within Southern California with the rest having transfers within the region for efficiency purposes. Additionally, shippers view trucks as more effective for lower distance trips compared to using rail as handling costs and transit times are less. Additionally, in this region, the competitiveness of truck has increased during the almost two decades spent for constructing the rail corridor resulting in lower cost and more ease of transporting load via truck. Another argument pertained to travel time savings from the rail corridor being lost from rail congestion elsewhere was inconclusive due to not enough empirical evidence. Additionally, terminal and drayage expenditures pertain to inherent intermodal expenditures that becomes counterbalanced by effective longer trips that are approximately greater than 1,000 miles. Then, above this threshold, intermodal becomes greater in competition against freight trucks, but below there are less instances of utilization of intermodal with barely any below 500 miles (Agarwal, Guiliano, Redfearn, 2004).

Discussion. When looking at the performance of the Alameda Rail corridor it is then apparent that as a goal for utilizing the facility as an approach to shifting freight truck over to rail trips, from the improvements from the facility itself, have been met with difficulty. In this respect, the project itself is not a panacea. Then, the current discussion delves into possible additional approaches that may holistically address the freight capacity issue at hand. Potential opportunities that exist pertain to improving the connecting segment regarding the process of loading the cargo onto the train and effectively unloading at the Vernon terminal. At the Port, this could involve constructing additional on-dock rail where cargo can be directly transferred from within the Port area to the Alameda Rail corridor. Resor and Blaze (2004) states, “…thus greatly reduce handling costs compared with those of a dray movement through public streets” (Resor & Blaze, p 50, 2004). From the review of literature, shippers’ and carriers’ mode choice leans towards freight trucks when the freight commodity has more value of time, distance is shorter than the threshold of 500 miles, and lower load amount. Accordingly, it is worthwhile exploring the possibility of allowing less than full load trips for materials that are high value and has greater value of time. This reduces the wait time and price can be reasonably set to reflect the expediency of the service. While there could be room for improvement on the transportation supply side, however, approaches to the demand side should also be considered.

The current author has previously driven on I-710 and trucks are able to utilize the highway facility without a toll. A thought includes approaches for trucks to internalize the negative externalities such as air pollution affecting the corridor communities. A possible approach relates to demand management through the conversion of a general purpose lane into a truck only toll lane where the revenue from tolls would assist with improving air quality such as installing zero emissions overhead wires for trucks traversing between the Port and the national railyard connection. It can also be used to fund improvement projects for enhancing the connection segments at the terminals to the Alameda Rail corridor.

 The table provided below reflect the upper five commodities that were imported in 2018 from The Port of Los Angeles:

The Port of Los Angeles

Top Five Containerized Imports of CY 2018 (in TEUs):

 

Commodity Type

Amount (TEUs)

Furniture

579,405

Auto Parts

373,934

Apparel

354,578

Footwear

233,157

Electronics

218,554

Source: The Port of Los Angeles (2019)

Additionally, regarding Port of Long Beach, the key imports includes “Crude oil, Electronics, Plastics, Furniture, and Clothing” (Port of Long Beach, p 2, n.d.). Overall, the commodities are not perishable and much of the commodities, in the list, are not necessarily time sensitive. However, the increasing popularity of one and two day shipping may have an effect on creating a situation where value of time increases[10]. This particular situation may increase the propensity of using truck especially for trips that are shorter distance. As a result, it is even more imperative to enhance the connecting terminals with the Alameda Rail corridor and further utilize technology to improve logistical coordination which includes an attempt in improving loading time for loading cargo on to the train.

The current paper presented a case study regarding the Alameda Corridor rail. Additionally, review of literature was performed regarding freight mode choice theory and lessons learned. Recommendations for possible approaches for improving the corridor were discussed. Overall, relying solely on the Alameda Corridor itself as a way to shift mode has not been a complete success and should not be viewed as a panacea to addressing freight highway capacity limitations. Rather, a holistic multi-approach consisting of addressing pricing and improving travel time such as through new on-dock rail, should be implemented. In light of the growth of ecommerce and popularity of two and one-day shipping, approaches that decrease the haul time to lower the distance threshold feasibility is necessary.

References

  • Alameda Corridor Transportation Authority. (2012). Alameda Corridor FACT SHEET. Retrieved April 22, 2019, from http://www.acta.org/projects/projects_completed_alameda_factsheet.asp
  • Alameda Corridor Transportation Authority. (2012). Alameda Corridor timeline. Retrieved April 22, 2019, from http://www.acta.org/projects/projects_completed_alameda_timeline.asp
  • Agarwal, A., Guiliano, G., & Redfearn, C. (2004). The Alameda Corridor: A white paper(Rep.).
  • Caltrans. (2012, July). Freight Planning Fact Sheet: Port of Long Beach. Retrieved April 22, 2019, from http://www.dot.ca.gov/hq/tpp/offices/ogm/ships/Fact_Sheets/Port_of_Long_Beach_Fact_Sheet_073012.pdf
  • Caltrans. (2013, February). Freight Planning Fact Sheet: Port of Los Angeles. Retrieved April 22, 2019, from http://www.dot.ca.gov/hq/tpp/offices/ogm/ships/Fact_Sheets/Port_of_Los_Angeles_Fact_Sheet_073012.pdf
  • Google. (2019). Google Maps. Retrieved April 22, 2019, from https://www.google.com/maps/@33.7603454,-118.2570366,14z?hl=en&authuser=0
  • Hicks, G. V. (1991). The Alameda Corridor: Meeting the challenge of port growth. Transportation Research Forum,31(2), 230-238.
  • Los Angeles County Metropolitan Transportation Authority, Port of Los Angeles, Port of Long Beach, & Alameda Corridor-East Construction. (2018). Southern California Rail Project – America’s Global Freight Gateway (pp. 1-330, Rep.).
  • Nam, K. (1997). A study on the estimation and aggregation of disaggregate models of mode choice for freight transport. Transportation Research -E (Logistics and Transportation Rev.), 33(3), 223-231. Retrieved May 16, 2019, from https://www.sciencedirect.com/science/article/pii/S1366554597000112.
  • Nelson, L. J. (2018, March 01). 710 Freeway is a ‘diesel death zone’ to neighbors – can vital commerce route be fixed? LA Times. Retrieved April 24, 2019, from https://www.latimes.com/local/lanow/la-me-ln-710-freeway-expansion-20180301-story.html
  • Park, M., Regan, A., & Yang, C. (2007). Emissions impacts of a modal shift: A case study of the Southern California ports region. Journal of International Logistics and Trade,5(2), 67-81.
  • Port of Long Beach. (n.d.). Facts at a Glance. Retrieved April 23, 2019, from http://polb.com/about/facts.asp
  • Prohaska, R., Konan, A., Kelly, K., & Lammert, M. (2016). Heavy-Duty Vehicle Port Drayage Drive Cycle Characterization and Development (2nd ed., Vol. 9, pp. 1-9, Conference Paper). SAE International Journal of Commercial Vehicles.
  • Resor, R. R., & Blaze, J. R. (2004). Short-Haul Rail Intermodal: Can it Compete with Trucks? Transportation Research Record: Journal of the Transportation Research Board, 1873, 45-52. Retrieved May 6, 2019.
  • Samimi, A., Kawamura, K., & Mohammadian, A. (2011). A behavioral analysis of freight mode choice decisions. Transportation Planning and Technology,34(8), 857-869.
  • Southern California Association of Governments. (2016). RTP SCS Transportation System: Goods Movement (pp. 1-80, Rep.). Los Angeles, California.
  • The Port of Los Angeles. (2019). 2018 Facts and Figures. Retrieved April 23, 2019, from https://www.portoflosangeles.org/business/statistics/facts-and-figures
  • Wang, Y., Ding, C., Liu, C., & Xie, B. (2013). An analysis of Interstate freight mode choice between truck and rail: A case study of Maryland, United States. Procedia Social and Behavioral Sciences, 96, 1239-1249. Retrieved May 15, 2019, from https://www.sciencedirect.com/science/article/pii/S1877042813022672.
  • Winston, C. (1981). A Disaggregate Model of the Demand for Intercity Freight Transportation. Econometrica, 49(4), 981-1006. Retrieved May 4, 2019, from https://www.jstor.org/stable/1912514.

Appendix A

Map of the Port of Los Angeles and Port of Long Beach

(Southern California Association of Governments, 2016)

Appendix B

Map of the Two Ports’ Terminals

(Prohaska, Konan, Kelly, and Lammert, 2016)

Appendix C

Map of Ground Transportation Connecting to the Port

(Los Angeles County Metropolitan Transportation Authority, Port of Los Angeles, Port of Long Beach & Alameda Corridor-East Construction, 2018)

Appendix D

  (Samimi, Kawamura, & Mohammadian, 2011)


[1] Please see Appendix A for maps of the two ports and Appendix B for the maps of the two ports’ terminals

[2] (Alameda Corridor Transportation Authority, 2012) Fact Sheet and timeline

[3] (Caltrans, 2012) and (Caltrans, 2013)

[4] (Caltrans, 2013) and (Google, 2019)

[5] Please see Appendix C for the map illustrating the I-110, I-710, and Alameda Rail Corridor

[6] Local is defined as “(Southern California, Southern Nevada, Arizona and New Mexico and southern portions of Utah and Colorado) (Southern California Association of Governments, 2016, p 9)

[7] Nam, p 229, 1997

[8] The definitions of the variables used in previous models by Samimi, Kawamura, and Mohammadian (2011) is provided in Appendix D

[9] (Park, Regan & Yang, 2007) and (Resor & Blaze, 2004)

[10] While a formal quantitative analysis for analyzing the potential effect of the popularity of one and two day shipping on freight behavior is ideal, however, this is out of the scope of the current paper and is left for future research.

Get Help With Your Essay

If you need assistance with writing your essay, our professional essay writing service is here to help!

Find out more

Cite This Work

To export a reference to this article please select a referencing stye below:

Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.
Reference Copied to Clipboard.

Related Services

View all

DMCA / Removal Request

If you are the original writer of this essay and no longer wish to have the essay published on the UK Essays website then please:

McAfee SECURE sites help keep you safe from identity theft, credit card fraud, spyware, spam, viruses and online scams Prices from
£124

Undergraduate 2:2 • 1000 words • 7 day delivery

Order now

Delivered on-time or your money back

Rated 4.6 out of 5 by
Reviews.co.uk Logo (188 Reviews)