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Innovations in Freight Demand Modeling and Data Improvement (2014)

Chapter:On the Evaluation of Incentive Structures Fostering Off-Hour Deliveries

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Suggested Citation:"On the Evaluation of Incentive Structures Fostering Off-Hour Deliveries." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Suggested Citation:"On the Evaluation of Incentive Structures Fostering Off-Hour Deliveries." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
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Page64
Page 65
Suggested Citation:"On the Evaluation of Incentive Structures Fostering Off-Hour Deliveries." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
×
Page65
Page 66
Suggested Citation:"On the Evaluation of Incentive Structures Fostering Off-Hour Deliveries." National Academies of Sciences, Engineering, and Medicine. 2014. Innovations in Freight Demand Modeling and Data Improvement. Washington, DC: The National Academies Press. doi: 10.17226/22336.
×
Page66

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

On the Evaluation of Incentive Structures Fostering Off-Hour Deliveries Felipe Aros-Vera and José Holguín-Vera Rensselaer Polytechnic Institute Presentation Notes: Presented by Felipe Aros-Vera, Rensselaer Polytechnic Institute. The model identifies the potential for off-peak deliveries to businesses in New York City, driven by congestion and a supply perspective. The question of how incentives can be used to encourage change in behavior for those producing externalities was analyzed. An important measure of transportation demand management (TDM) involves using public-sector incentives to include a change of delivery times from regular hours to off-hours. Typically, the truck is not making the decision of delivery time even as tolls were increased for truckers; rather, it is client demand that requires behavior change. The objective was to simulate the carriers’ and receivers’ joint decision process to evaluate TDM policies and determine whether receivers will accept incentives to move delivery times. There were three types of incentives analyzed: business support, public recognition, and one-time incentives. Data were taken from the New York Metropolitan Transportation Council (NYMTC) regional transportation model. Abstract This paper develops a methodology to evaluate incentive structures for implementing off-hour deliveries (OHD) programs. Using a behavioral microsimulation (BMS) to reproduce the joint decision of carriers and receivers toward OHD, the methodology determines the percentage of trucks and deliveries that would switch to the off-hours and the budget required for the implementation. The behavior of receivers is modeled using discrete choice models, and the behavior of carriers depends on the cost comparison between the base case scenario and the new delivery network that emerged after the receivers’ decisions. The results are analyzed in conjunction with previous theoretical research regarding the benefits of providing different combinations of incentives and a targeted set of receivers according to geographic location or industry segment. The methodology is applied to the freight industry in Manhattan. The paper delivers policy recommendations that shed light into the implementations of OHD programs. Introduction Congestion has serious economic implications in terms of productivity losses and pollution. So far, congestion has been tackled mostly from the supply perspective by enhancing infrastructure or operations. However, as it has been widely documented, in the long term, such an approach increases demand for transportation that brings the situation to the same place where it started. An alternative is to use transportation demand management (TDM) initiatives, which influence users’ behavior to bring the demand to the level that infrastructure can optimally accommodate. Examples of car TDM include congestion pricing, carpooling programs, and high occupancy 63

lanes. Despite the relatively long tradition of car TDM, similar measures for urban freight are very rare. The heart of the issue when implementing freight TDM is that the generation of freight demand is largely the work of shippers, producers, and receivers, as the carriers are primarily a conduit between the supply and the demand for freight. In this context, freight TDM entails inducing behavior changes on the receivers of the supplies, who are the ones who need the supply as input for their economic activities. Only recently, however, the research community has realized the important role that receivers play. For the most part, transportation policy has focused on the carriers, overlooking the fact that, though freight traffic is what materially produces the externalities, it is the demand for freight at the receiver sites that creates the freight traffic. As a result, policy initiatives that target the receivers, who are the actual decision makers, are more effective than policies that focus on the carriers, who play a secondary role. The challenge is, however, that estimating how receivers and carriers would react to such incentives is a complex endeavor. While modeling the behavior of receivers could seamlessly be done with discrete choice models, modeling how the carriers would react to the receivers’ response to the incentives is significantly more complicated. In fact, the carrier’s response depends on the structure of the delivery network that would arise, the number of receivers switching to OHD, and the unit transportation costs, among many other variables. Another important aspect is the structure of the incentives to be provided. The incentives could target receivers in all or some specific industry sectors, or receivers located in a congested area. Moreover, the incentive amount could be the same to all receivers or could be different depending, for instance, on the number of deliveries switched to the OHD. The main objective of this paper is to use a behavioral microsimulation (BMS) to (1) assess the different combination of incentives and (2) analyze the effectiveness of targeted monetary incentives for receivers in specific geographic locations and industry segments. The paper briefly reviews the literature on OHD; describes the methodology and its application to the case of New York City; and delivers conclusions and policy recommendations to shed light on the implementation of OHD programs. Methodology The methodology developed in this paper is based on a behavioral microsimulation (BMS). The BMS generates the carrier and the set of receivers on the Carrier/Receiver Synthetic Generation. This module selects an industry segment and, according to its characteristics, determines the number of receivers and their locations so that the entire simulation reproduces the geographic distribution of businesses in the area under analysis. The receiver behavioral simulation determines the decision of the receivers in response to incentives using discrete choice models. Finally, the BMS models the decision of the carrier, using the receivers’ choices and the cost of delivering as inputs. This process is repeated for a large number of carrier-receivers combinations. 64

The root of the complexity is that, in response to an incentive, the receivers in a delivery tour react differently: some receivers will accept OHD, and others will reject the idea. Once the receivers decide how they would react to an incentive, the carrier decides what to do. From the carrier’s perspective, two delivery networks arise: one for the base case, where all receivers are in the regular hours, and another one with a mixed operation. The carrier will do OHD only if the mixed operation has a lower cost than the base case. In general, if only a handful of receivers want OHD, the additional delivery tour required in the mixed operation is likely to produce an increase in costs leading the carrier to reject OHD. Conversely, if a large number of receivers accept OHD, the carrier is likely to go along with their request, as it will lead to cost savings. The BMS considers three incentives: one-time incentive (OTI), business support (BS), and public recognition (PR). These incentives can be handed out to all receivers in Manhattan or some receivers according to industry segment or geographic location. The combination of type of incentive and how the incentives are handed out is denominated a structure of incentives. Each of these structures is analyzed according to three performance measurements: (1) the percentage of trucks switching to OHD, denominated joint market share (JMS) since it is the result of the joint decision of carriers and receivers; (2) the percentage of deliveries switching to OHD, denominated receivers market share (RMS); and (3) the budget required to provide the incentives to receivers, which is determined based on the RMS. Results for Manhattan and Policy Recommendations A monetary incentive of $1,000, in combination with business support and public recognition to receivers in Manhattan, could move more than 2,300 deliveries to the night hours; this corresponds to a reduction of 2% of deliveries. The budget required for the incentives is about $2.4 million. Therefore, the cost of the program has to be compared with the social and economic benefits of moving these deliveries to the off-hours. In the case of Manhattan, each delivery is estimated to take between 45 and 90 minutes during day hours. If the incentive reaches $10,000, more than 8,000 deliveries could be moved to the night. Consequently, the benefits must be compared with the $70 million of the program. The analysis of industry-oriented incentives reveals the benefits of giving incentives to retail, which produces almost 60% of freight trips in Manhattan. However, the most remarkable results come from geographically oriented incentives. In this case, incentives to receivers located in the most congested parts of the city—lower and midtown Manhattan—have the largest economic and social benefits. For instance, an incentive of $10,000, requiring $36 million, could move around 4,100 deliveries, similar numbers to giving incentives to the entire city, with the exception that these deliveries are made in the most congested part of the city. The effectiveness of the investment can be confirmed using a simple measure, the ratio between the percentage of carriers moving to OHD and the budget. The ratio shows that giving incentives to the most congested part of the city is, on average, 30% more effective. These results are aligned with previous theoretical research that encourages the application of geographically focused incentives to foster off-hour deliveries. 65

The analyses produced in this paper are very important and encouraging. To start with, they show the potential impact of providing incentives to receivers as a way to implementing OHD programs and the importance of giving monetary incentives in conjunction with other types of incentives that recognize the good practice in the industry. Second, they confirm that geographically focused incentives are more effective than other policies. In addition, these findings suggest that multi-year policies aimed at fostering unassisted OHD could gradually achieve major reductions in truck traffic in the regular hours. The reason is that, according to the experience in New York City, the receivers that try unassisted OHD tend to stay in the program after the termination of the incentive. If this trend continues, it could mean that multi-year incentives programs would be able to keep increasing the market share of OHD, though a point could be reached where no more increases are possible. These are, nevertheless, promising developments that highlight the potential of freight TDM. 66

Next: Analyzing Future Freight Challenges in Maryland Using Freight Data Sources and the Maryland Statewide Transportation Model (MSTM) »
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TRB’s second Strategic Highway Research Program (SHRP 2) Report: Innovations in Freight Demand Modeling and Data Improvement provides detail to the events of "The TRB Second Symposium on Innovations in Freight Demand Modeling and Data," which took place October 21-22, 2013. The symposium explored the progress of innovative freight modeling approaches as recommended by the Freight Demand Modeling and Data Improvement Strategic Plan.

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