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From page 151... ... 151 This chapter illustrates some of the possible applications of the mode choice models estimated as a part of this project. To give the numerical experiments a real-life flavor, the research team used some of the cases studies conducted as inspiration for the numerical experiments. Read the entire page →
From page 152... ... 152 Impacts of Policy-Induced Freight Modal Shifts shipment patterns at specific corridors. If the shipment-size data for a given corridor are small or of questionable quality, the shipment-size models may provide the best option. Read the entire page →
From page 153... ... Source: (NS 2014b) Figure 45. Read the entire page →
From page 154... ... 154 Impacts of Policy-Induced Freight Modal Shifts 0% 1% 2% 3% 4% 5% 6% 7% 8% 9% M ar ke t S ha re via Knoxville via Harrisburg via Heartland 35 - El ec tro nic 34 - M ac hin ery 30 - Te xti les 40 - M isc ell an eo us 24 - Pl as tic s a nd ru bb er 5 M ea t 8 A lco ho lic be ve rag es 4 A nim al fee d 33 - Ar tic les of ba se m eta l 29 - Pr int ed pr od uc ts 26 - W oo d p ro du cts 43 - M ixe d f rei gh t Figure 46. Rail market share, P(R) Read the entire page →
From page 155... ... Numerical Experiments 155 explained, in part, by the small amount of data in PUMS for that commodity (only 35 observations) Read the entire page →
From page 156... ... 156 Impacts of Policy-Induced Freight Modal Shifts along the Crescent Corridor. For the analysis, the origin selected was Metairie, Louisiana. Read the entire page →
From page 157... ... Figure 48. Truck market shares, shipment-size models, all commodities. Read the entire page →
From page 158... ... Figure 50. Truck market shares, shipment-size models, plastics and rubber (SCTG 24) Read the entire page →
From page 159... ... Numerical Experiments 159 Figure 52 shows the truck market share patterns for mixed freight (SCTG 43) Read the entire page →
From page 160... ... Figure 52. Truck market shares, shipment-size models, mixed freight (SCTG 43) Read the entire page →
From page 161... ... Figure 54. Truck market shares, shipment-size models, machinery (SCTG 34) Read the entire page →
From page 162... ... Figure 56. Truck market shares, shipment-size models, wood products (SCTG 26) Read the entire page →
From page 163... ... Figure 58. Truck market shares, PUMS shipment size, all commodities except plastics (SCTG 24) Read the entire page →
From page 164... ... 164 Impacts of Policy-Induced Freight Modal Shifts Figure 60 presents the truck market share estimated for textiles (SCTG 30) Read the entire page →
From page 165... ... Figure 61. Truck market shares, PUMS shipment size, mixed freight (SCTG 43) Read the entire page →
From page 166... ... 98.0% 98.2% 98.4% 98.6% 98.8% 99.0% 99.2% 99.4% 99.6% 0 200 400 600 800 1000 1200 1400 T ru ck M ar ke t S ha re GCD (miles) Read the entire page →
From page 167... ... Figure 64. Truck market shares, PUMS shipment size, base metal (SCTG 32) Read the entire page →
From page 168... ... 168 Impacts of Policy-Induced Freight Modal Shifts For presentation purposes, the results were aggregated by taking the mean value of all the probabilities corresponding to records that lie within a specific interval of driving time by truck. The intervals were constructed starting at 0 and with a length of 4.5 hours. Read the entire page →
From page 169... ... Numerical Experiments 169 State of Origin Zip Code Sample Size Illinois 60629 18,314 Ohio 45856 16,628 Philadelphia 16874 15,656 Table 58. Sample sizes for the top three origins chosen in the Heartland Corridor. Read the entire page →
From page 170... ... 170 Impacts of Policy-Induced Freight Modal Shifts 75% 80% 85% 90% 95% 100% 0 5 10 15 Pr ob ab ili ty o f c ho os in g Tr uc k Driving time by truck (h) All Animals and Agriculture Grains, Oils,… Chemicals/Pharmaceutical… Wood, Paper, Textiles… Non-metallic/Metallic Materials Electronic/Electric Equipment Furniture and Miscellaneous… Building stone… Coal, Petroleum,... Read the entire page →
From page 171... ... Numerical Experiments 171 Origin ZIP Code: 60629 (Illinois) 70% 75% 80% 85% 90% 95% 100% 0 5 10 15 Pr ob ab ili ty o f c ho os in g Tr uc k Driving time by truck (h) Read the entire page →
From page 172... ... 172 Impacts of Policy-Induced Freight Modal Shifts General Findings. For the scenario of Philadelphia, the results indicate that most of the commodities exhibit a strong preference for trucks. Read the entire page →
From page 173... ... Numerical Experiments 173 Origin ZIP Code: 07104 (New Jersey) 94% 95% 96% 97% 98% 99% 100% 0 5 10 15 20 25 P ro ba bi lit y of c ho os in g T ru ck Driving time by truck (h) Read the entire page →
From page 174... ... 174 Impacts of Policy-Induced Freight Modal Shifts Origin ZIP Code: 30318 (Georgia) 94% 95% 96% 97% 98% 99% 100% 0 5 10 15 20 25 Pr ob ab ili ty o f c ho os in g Tr uc k Driving time by truck (h) Read the entire page →
From page 175... ... Numerical Experiments 175 Origin ZIP Code: 16874 (Philadelphia) 95% 96% 97% 98% 99% 100% 0 5 10 15 20 25 Pr ob ab ili ty o f c ho os in g Tr uc k Driving time by truck (h) Read the entire page →
From page 176... ... 176 Impacts of Policy-Induced Freight Modal Shifts General Findings. For the case of New Jersey, the group of wood, paper and textiles is the most sensitive group no matter what the source for the shipment data are. Read the entire page →

From page 151...

... 151 This chapter illustrates some of the possible applications of the mode choice models estimated as a part of this project. To give the numerical experiments a real-life flavor, the research team used some of the cases studies conducted as inspiration for the numerical experiments.