The final task of this research is to use the new modeling system to conduct scenario analyses. Our efforts in updating the model showed that it is not robust enough for the detailed analysis and forecasting required for long range regional planning. However, the model can be used at the sketch planning level to compare different scenarios. The scenarios can be of two types: exogenous economic changes and system management changes. We selected three scenarios to demonstrate the ways that a modeling system such as this could be used in sketch planning.
The three scenarios tested are 1) reduce international trade by 25%; 2) shift 25% of truck trips to rail; 3) implement peak pricing for all port truck trips. Scenario 1 approximates what happened in the region following the financial crash of 2007 and the resulting recession. Scenario 2 addresses the question of what would happen if we were able to shift longer distance truck trips to rail. Shifting truck traffic to rail is widely advocated as a means to reduce congestion, air pollution, and energy consumption. Scenario 3 approximates the PierPass program, which charges a fee for entry to the ports during peak hours. In each case we compare results to the baseline to examine freight flow changes.
We modeled Scenario 1 by reducing international imports and exports by 25%. Referring back to Figure 3.1 in Chapter 3, these are the "LA to ROW" and "ROW to LA" flows. All other flows remain the same. We run the Argos workflow to generate a new inter-regional truck O-D matrix, and then use TransCAD to run the new equilibrium assignment. In order to test the quality of our updated model results, we expanded the equilibrium assignment results to 24 hours in order to compare with the actual truck count data (see Chapter 3). We do not need to do this for the scenarios, as the basis of comparison is the model baseline. Thus our results are for the AM Peak.
Table 4.1 gives results for total number of truck trips, truck VMT, and truck VHT for the AM peak equilibrium assignment. Reducing international flows by 25% yields a reduction of truck trips of around 4%. This reflects the relatively small share of truck flows associated with international trade. It should be noted that we do not take into account the indirect and induced economic effects of this trade loss, hence it is a conservative estimate of impacts. It would be necessary to re-run the regional input/output model in order to capture the full economic effects, which is beyond the scope of this research.
|Truck trips||Truck VMT||Truck VHT|
Table 4.2 and Figure 4.1 show how the reduced truck flows are distributed across the screenlines. In this case the reduction is evenly distributed. Why should this be the case? By definition, these are flows that either originate or end somewhere in the region (these are not the international flows that originate or end somewhere else in the US). Recall that we use economic activity to locate these origins and destinations. Since economic activity is spread throughout the region, the trade-related reduction is also spread throughout the region.
|Screenline||Base case||Reduced Int'l trade||Difference||% Difference|
Scenario 2 is modeled by taking the truck portions of domestic imports and exports and shifting 25% of the truck flow to rail across all industry sectors. These are the "LA2US" and "US2LA" flows in Figure 3.1, Chapter 3. We do not consider international imports and exports, because none of these are truck flows in the baseline. That is, imports and exports arrive/depart only from ports or airports. This scenario is equivalent to saying, what if rail were more competitive with truck and could capture more long-haul traffic?
Because every export that originates in the region and every import that has a final destination in the region, whether domestic or international, has a "last mile" truck trip, the total number of truck trips does not change in this scenario. Rather, 25% of the trips that were entering or exiting the region are now entering or exiting by rail, hence reducing truck VMT within the region. Table 4.3 gives summary results. Truck travel drops substantially as a result of the mode shift, because long distance truck trips are removed from the network.
|Truck trips||Truck VMT||Truck VHT|
Table 4.4 and Figure 4.2 show results by screenline. It can be seen that the reduction in trips is not consistent across screenlines. There are increases at some and decreases at others. As would be expected, truck trips around the rail nodes increase (for example screenlines 2 and 3; see Figure 3.3 in Chapter 3), because some trips are diverted towards the rail nodes rather than traveling through the region. Truck trips at screenlines located on the major routes into and out of the region decrease (for example screenlines 18 through 21), reflecting the same dynamic. Figure 4.3 shows the results on the highway network. Changes on links of 500 PCE or more are shown; green represents decreases from the baseline, and red represents increases. The size of the change is represented by the width of the line. Figure 4.3 illustrates the rather large changes on the main highway routes into and out of the region.
|Screenline||Base case||Truck2Rail||Difference||% Diff|
The region's ports have been very innovative in seeking ways to reduce port-related truck traffic. From around 2004, the ports faced strong political pressure to address the growing congestion and pollution problems associated with growing trade. In 2005 the ports implemented PierPass, which charged a fee of $40 per TEU (twenty foot equivalent unit) for containers entering the ports during daytime hours. The following year, the fee was raised to $50. The typical container is 2 TEUs, so the fee is about $100. This fee is charged to the beneficial cargo owner, not the trucker. Truck tolls have been discussed in other contexts, for example on truck only facilities, or on facilities where truck traffic constitutes a larger than average share of total traffic.
Scenario 3 estimates the impact of a toll of $45 per truck for all trucks entering the ports during the AM peak. We have no information on who owns the cargo, so we cannot simulate the PierPass program. We assume that the toll is imposed on the truck, and hence the truck (driver) chooses whether to make the trip during the peak or shift to another time period. We use parameters from existing studies for the truck travel demand function: value of time is $47/hour, price elasticity of demand is -0.84, and generalized running costs are $1.10 per truck mile (Zhou, 2010). We asssume a "before tolls" average speed of 31.1 mph, which is based on the SCAG regional model data (SCAG, 2007). Our starting point is the unpriced demand estimated in the baseline, and we assume no change in the level of trade activities.
This scenario is modeled outside the Argo workflow; we take the initial truck demand as given from the baseline O-D matrix. We identity the truck flows to and from the ports, and then estimate a truck demand model based on the parameters described above. We then adjust the baseline O-D matrix to reflect the change in truck trips to/from the ports and proceed to a new network multi-user equilibrium assignment, combining the new truck PCE O-D matrix with the baseline passenger O-D matrix. . Based on Salas et al (2008), We use the following formula to calculate the truck demand reduction where there is a toll:
Tit is the truck flows between TAZ i and the port,
τ is the toll charged on the truck, which is also the assumed change in generalized cost before and after the tolling,
GCi is the overall generalized cost per truck from TAZ i to the port before the tolling,
ε is the toll-price elasticity for trucks, in our case it is -0.84.
GCi is calculated as:
TTi is the average travel time between TAZ i and the port before the tolling,
VOT is average value of time for trucks,
RC is the average running cost per truck per mile,
Di is the distance in miles from TAZ i to the port.
Summary results are given in Table 4.5. it can be seen that pricing has less impact on the region than one might expect. This is due to the small share of port entry trips in the total trip matrix - about 3%. The screenline results show that the change is concentrated around the ports (screenlines 2 and 3), as would be expected. If we look at the port entries, we find that AM peak truck entries decline by about 50%. Thus the effects in the immediate vicinity of the ports is quite large. Figure 4.4 shows changes in flows on the network around the ports.
|Truck trips||Truck VMT||Truck VHT|
|Screenline||Base Case||Port Pricing||Difference||% Difference|
We have demonstrated that the Argos system can be used to conduct scenario analysis. Exogeneous economic changes as well as policy changes can be modeled. Results from the three scenarios are informative. A reduction in Los Angeles based international trade results in a modest reduction in truck traffic throughout the region. Had we taken into account the indirect impacts of trade losses, the reduction would have been larger. Shifting longer distance traffic from rail to truck has the largest impact on total truck VMT, because the longest trips are being removed from the network. Port entry pricing has a significant impact around the ports, but a modest impact on the region overall. These scenarios demonstrate the utility of having a relatively easy and quick method to test alternative policies or assumptions about regional economic activity.
Use of Argos by practitioners requires a user friendly interface for manipulating the input data and generating new results. Therefore, we developed a web-based graphical user interface that allows a practioner to explore different scenarios. The interface allows the user to modify several parameters and run the Argos planner under those conditions automatically. The interface then presents the resulting attractions and productions computed by the Argos workflow under the specified condtions. Specifically, we considered the following scenarios: