traffic impact analysis v2

TRAFFIC IMPACT ANALYSIS OF AN URBAN OFF-HOUR DELIVERY PROGRAM 1 USING MACROSCOPIC AND MESOSCOPIC SIMULATION MODELS 2

Shrisan Iyer (corresponding author) 3 Research Associate, 4 Rutgers Intelligent Transportation Systems Laboratory 5 Rutgers, The State University of New Jersey 6 CoRE 736, 96 Frelinghuysen Road, Piscataway, NJ 08854 7 Tel: +1 732 445 6243 x274, Fax: +1 732 445 0577, Email: [email protected] 8

Kaan Ozbay, Ph.D 9 Professor & Director, 10 Rutgers Intelligent Transportation Systems Laboratory 11 Rutgers, The State University of New Jersey 12 Department of Civil and Environmental Engineering 13 623 Bowser Road, Piscataway, NJ 08854 14 Tel: +1 732 445 2792, Fax: +1 732 445 0577, Email: [email protected] 15

Satish Ukkusuri, Ph.D 16 Associate Professor, 17 School of Civil Engineering, Purdue University, 18 550 Stadium Mall Drive, West Lafayette, IN 47907-2051, USA 19 Tel: +1 765 494 2296, Fax: +1 765 494 0395, Email: [email protected] 20

Wilfredo F. Yushimito 21 Graduate Research Assistant, 22 Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 23 110 Eighth Street, 5107 Jonsson Engineering Center, Troy, NY 12180-3590, USA 24 Tel: +1 518 276 8306, Fax: +1 518 276 4833, Email: [email protected] 25

Ender Faruk Morgul 26 Graduate Research Assistant, 27 Rutgers Intelligent Transportation Systems Laboratory 28 Rutgers, The State University of New Jersey 29 Department of Civil and Environmental Engineering 30 623 Bowser Road, Piscataway, NJ 08854 Email: [email protected] 31

Jose Holguin-Veras, Ph.D 32 Professor, 33 Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute, 34 110 Eighth Street, 4030 Jonsson Engineering Center, Troy, NY 12180-3590, USA 35 Tel: +1 518 276 6221,Fax: +1 518 276 4833, Email: [email protected] 36

Abstract: 115 words 37 Word count: 4,694 Text + 9 Figures + 1 Table = 7,194 words 38 Submission Date: August 1st, 2010 39 Revised Submission Date: November 15th, 2010 40

41

TRB 2011 Annual Meeting Paper revised from original submittal.

Iyer, Ozbay, Ukkusuri, Yushimito, Morgul, and Holguin-Veras 1

ABSTRACT 42

This paper describes the usage of a regional travel demand model and an extracted mesoscopic 43

sub-simulation model in tandem to model and observe the traffic impacts of an off-hour delivery 44

program. The study is based on OHD-participation behavioral data for the New York City 45

borough of Manhattan, with traffic impacts measured throughout the New York metropolitan 46

region. Analysis is conducted to determine the effectiveness and impacts of the scenarios 47

modeled; focusing on the changes predicted by the traffic models, with link travel time and speed 48

changes as the key measure of output. The results from both models are compared and analyzed, 49

and a discussion on the usage of these models for this purpose is presented. 50

51



INTRODUCTION 52

Freight traffic constitutes a significant component of a region’s traffic system. Given its 53

considerable increase in recent years, commercial vehicles (CMVs) have become a primary 54

target for planners and policymakers in mitigating traffic congestion. Much work has been done 55

regarding the quantification of the traffic impacts of congestion mitigation programs but less has 56

been conducted concerning programs targeted specifically at trucks or commercial vehicles. 57

Programs addressing highway freight transportation problems are new and experimental, with 58

only a handful of cities implementing congestion control measures (1). The effects of trucks and 59

commercial vehicles on highway networks are known to be negative, specifically causing 60

congestion due to their nature as large vehicles generally traveling at a slower speed than 61

automobile traffic. Studies have shown that truck traffic negatively affects the flow rate of 62

highways and local roads, thereby causing congestion on roadways with high traffic volumes (2). 63

Holguin-Veras et al. (2006) determined that freight traffic generated by delivery vehicles to 64

city businesses not only contributed to congestion but caused added problems due to double-65

parking and blockages as a result of the lack of parking spaces during the day-time, peak, 66

delivery hours (3, 4). These claims are supported by research into the effects of illegal parking on 67

traffic congestion which show that illegal parking (primary conducted by CMVs) causes 68

significant capacity losses to roadways, which produce more severe effects during peak hours 69

than during off-peak hours (5). As a result, policy makers have sought to control truck and 70

commercial vehicle traffic, particularly within cities’ central business districts, with either value 71

pricing measures or by introducing off-peak delivery programs. Both ideas are gaining popularity 72

in the United States but their degrees of success are yet unknown. 73

An off-hour delivery (OHD) program is expected to provide benefits to the highway 74

network, since fewer trucks and commercial vehicles during congested hours would improve 75

highway speeds and decrease travel times. However the precise effects are unknown and their 76

estimation is difficult. Freight planning models or highway networks can be developed and run 77

in simulation software but these processes are both time-consuming and require extensive data 78

procurement. Instead many city transportation agencies or metropolitan planning organizations 79

already have developed traffic planning and simulation models. In order to assess the impact of a 80

freight targeted-policy program, it is important to consider the effects to all classes of vehicles 81

and the entire transportation system. While some domestic freight is moved by rail, a significant 82



percentage of goods are transported by trucks and delivery vans, which use the same highway 83

infrastructure as all other vehicles. This paper focuses solely on trucks and delivery vans, and 84

since any changes to their travel patterns will affect the entire highway network, this paper also 85

considers the effect on turn passenger cars. To fully assess traffic impacts throughout the region, 86

a model is required that considers both passenger and freight travel. Comprehensive travel 87

demand models developed by state and regional agencies consider many trip types and many 88

modes. This paper uses a regional travel demand model to estimate the effects of an off-hour 89

delivery (OHD) program. 90

Yannis, et al. (2006) used a transportation model to estimate the impacts of an OHD program 91

in the city of Athens. They presented a methodology to model modified commercial vehicle 92

origin-destination (OD) demands on a highway network when delivery operations within the city 93

of Athens were restricted (6). The study described modeling of the city’s roadways under 94

observed and modified commercial vehicle demands, accomplished by first observing existing 95

traffic conditions to collect sufficient data to build a comprehensive roadway network, 96

calibrating the collected data against actual conditions, and implementing the OHD program 97

changes into the model. Using SATURN, they were able to conduct traffic assignment based on 98

actual (base) traffic demands and again with modified demands, which were caused by 99

restricting delivery vehicles from entering the study area within certain times of day. It is 100

important to note that SATURN is strictly a traffic assignment model, not a large scale travel 101

demand model (7). 102

Yannis et al. created a network that consisted of 285 production and attraction zones, with 103

demand matrices for six separate time periods throughout the day. Additionally land uses and 104

average stop times were also studied to represent the effect that actual delivery activities have on 105

roadways, which manifest themselves in the forms of double parking or lane blockages. The 106

researchers were able to code these activities into the network and thus modify the capacity of 107

certain roads where deliveries were taking place. The simulations showed that by barring 108

delivery vehicles from the study area from 7:00am–10:30am, simulated average roadway speeds 109

increased by 4.7%, and a similar restriction from 2:00pm–4:30pm increased simulated average 110

speeds by 1%. Conversely, the average speed for the 10:30am–2:00pm period decreased by 5.8% 111

as the displaced delivery vehicles were assumed to use this period to enter the study area. 112

However the researchers noted that the increase in speeds during the morning and afternoon 113



periods had a greater benefit than the loss in the midday periods, due to higher traffic volumes in 114

the morning and afternoon. 115

This paper describes the usage of the New York Best Practice Model (NYBPM), a regional 116

planning model developed by New York Metropolitan Transportation Council (NYMTC), to 117

study the impacts of a proposed off-hour delivery program on the traffic network of the New 118

York City metropolitan region (8). The availability of NYBPM eliminates the need to create a 119

new network model for this study. Some of the shortcomings of a large-scale travel demand 120

model can be overcome by extracting a detailed localized sub-model which can be used for 121

further analysis in a mesoscopic simulation tool. In this case, a mesoscopic sub-network of 122

Manhattan, the most densely populated and commercial county in the city and region, is 123

extracted from the NYBPM for detailed simulation and analysis. Once the models’ results are 124

obtained analysis is conducted to determine the effectiveness and impacts of the off-hour 125

delivery scenarios modeled. The results from both models are compared and analyzed, and a 126

discussion on the usage of these models is presented. 127

The OHD program studied provides incentives for businesses within Manhattan to accept 128

their deliveries during the off-peak overnight hours instead of the daytime hours. Data from prior 129

behavioral studies is used to determine the percentage of business willing to accept incentives 130

based on a tax incentive amount, from which scenarios are constructed to measure traffic impacts 131

based on levels of receiver participation. Based on the receiver participation levels, CMV traffic 132

corresponding to the receivers’ deliveries is shifted from the daytime to the overnight hours 133

within the traffic models used. The models are then run with modified demands and the outputs 134

are compared to the base (existing) case to analyze the potential effects of the OHD program. 135

The paper is organized as follows: first an overview of each of the models and 136

methodologies used is provided, then the scenarios studied and modeled in both the NYBPM and 137

the mesoscopic simulation models are detailed, and the findings and results of each presented. 138

Finally a comparison of traffic impacts obtained from both models is conducted before 139

conclusions are discussed. 140

141



METHODOLOGY 142

Models Utilized 143

If commercial vehicle traffic is shifted from one time period of day to another – from peak 144

periods to off-peak periods – it is critical to observe whether there is a measurable improvement 145

to the remaining traffic conditions during the peak periods, and conversely whether off-peak 146

conditions are significantly disrupted. As mentioned, the New York Best Practice Model 147

(NYBPM), the travel demand model used by New York-area planners (9), is employed for 148

region-wide analysis. It covers 28 counties in the New York area, and allows for studying the full 149

effects of the OHD program to the complete highway network of New York City and its 150

surrounding areas. 151

NYBPM is particularly useful for analysis of the changes and redistributions of truck travel 152

patterns, since it utilizes TransCAD’s multi-modal, multi-class, assignment feature (9). The input 153

origin-destination (OD) matrices for highway assignment are six-fold, one for SOV, HOV2, 154

HOV3+, truck, and other commercial. In the multi-class assignment, each trip class is treated 155

separately and utilizes its own cost or volume-delay function, and classes prohibited on certain 156

links are accounted for. Cars and trucks are assigned separately, but still allowed to find the best 157

route to minimize their cost. However it should be emphasized that NYBPM was not designed 158

specifically for or with an emphasis on freight modeling. NYMTC is currently studying 159

alternative ways to study freight transportation and plan for future changes (10). 160

The assignment portion of the model is a collection of four models for four periods of the 161

day. The four periods, each with their own networks and origin-destination (OD) matrices, are 162

AM Peak Period (AM; 6–10am), Midday Period (MD; 10am–3pm), PM Peak Period (PM; 3–163

7pm), and Overnight Period (NT; 7pm–6am). The defined network periods and separate OD 164

matrices allow for modeling to be based on shifting demand from the daytime periods to the 165

overnight period, as described by the OHD program. Changes to CMV (trucks and other 166

commercials) behavior are represented by manipulating the number of CMV trips between each 167

origin-destination pair for each time period. Once the existing CMV OD matrices within 168

NYBPM are altered the models’ outputs are compared to the base-case. The NYBPM modeling 169

methodology is illustrated in Figure 1. 170



NYBPM

Sub-Network Extraction

Sub-Network Calibration

Hourly Time Dependent OD

Matrices Calibration

Simulation

Dyn

amic

Dat

a

Comparative Analysis of Traffic Impact

Scenario Hourly Time Dependent OD

Matrices

Simulation

Behavioral M

odel

Scenario Generation

Base Case Scenarios

Calibration

Calibrated NYBPM OD Matrices

Calibrated NYBPM

Shift Scenarios AssignmentNYBPM Base-Case Assignment

Comparative Analysis of Traffic Impact

BMS Data

Shift Scenario OD Matrices Shift Model

NYBPM

Mesoscopic Simulation 171

Figure 1: Research Methodology 172

173



The New York Best Practice Model, running in TransCAD, also allows for transfer of the 174

highway network to TransModeler, both software tools developed by Caliper. While TransCAD 175

is a static traffic assignment tool, TransModeler is a traffic simulator that is capable of 176

performing simulations at a microscopic and mesoscopic level. In order to conduct more detailed 177

analysis at the vehicle-level, the simulation is performed on an extracted sub-network of 178

Manhattan and its access facilities (bridges and tunnels). Figure 2 shows the NYBPM highway 179

network and the sub-network of Manhattan used for mesoscopic simulation. 180

The methodology for mesoscopic simulation consists of two simultaneous procedures 181

(Figure 1): developing a model for the base case and scenario generation. Since NYBPM has 182

been designed for modeling macro-scale static behavior, it cannot be directly used as an input for 183

meso-simulation, since only major intersections in the local network have intersection geometry 184

details. However, it is still appropriate for mesoscopic simulation provided that the NYBPM has 185

the necessary road class information, free flow speeds, capacities, number of lanes, etc. Hence 186

the network can be imported directly to TransModeler for the main features of the network. Once 187

imported, the network is calibrated to assure a minimum exit-flow and avoid queues and 188

spillovers. 189

190

Figure 2: NYBPM Highway Network with Manhattan Sub-network Highlighted 191



The mesoscopic simulation uses hourly OD matrices, with three classes being simulated: 192

passenger cars, trucks, and other commercial vehicles (pickups, vans). In all cases, simulations 193

are performed by the four general time periods with preloading the state of the previous 194

simulation period (e.g., end-state of AM is preloaded for MD). The route choice model uses the 195

default stochastic shortest path with response to high delay embedded in TransModeler (11). 196

Under this method, paths costs are randomized based on different parameters such as perception 197

of shortest path error and behavior parameters that have been calibrated according to the 198

available data. The simulation also reroutes vehicles when their delays (different with respect to 199

their free flow travel times) are too high. Thus the paths are initially fixed but are allowed to vary 200

when congestion is high. However since the simulation requires a good estimate of the travel 201

times, historical travel times are obtained from the base case using a sample of five runs and 202

aggregating the travel times following an averaging procedure akin to the method of successive 203

averages - MSA (11). 204

205

Shift Model 206

A model is developed to translate off-hour delivery participation levels into traffic demand 207

changes, to study the traffic effects of the OHD program. Based on previous studies (12, 13, 14), 208

there is a percentage of receivers (the data available is restricted to food and retail industries) 209

within Manhattan willing to accept annual tax incentives to shift their delivery operations to the 210

off-peak hours (assumed as 7pm–6am). In this section, we present the scenario generation 211

assuming that different tax incentives are used to implement OHD. The resulting scenarios 212

provide a shifting percentage of commercial vehicles that are shifted to the off-peak hours. In 213

order to understand which types of industries are more likely to be willing to implement off-214

hours deliveries, behavioral modeling results were obtained by applying discrete choice models 215

to scenarios discussed in detailed in Holguin-Veras et al. (13, 14). These scenarios were intended 216

to assist in determining the types of policies that would have the greatest impact upon the 217

willingness of carriers and receivers to participate in OHD by asking receivers how likely they 218

would be to accept a certain percentage of their deliveries during the off-hours in return for a tax 219

deduction. 220

The estimates are provided for every zip code in Manhattan, which for simplicity are 221

grouped into community board groupings. There are four main community board groupings in 222



Manhattan, each divided according to land use. The first two community board groupings (1-3 223

and 4-6) represent the two main central business districts of Manhattan, Downtown and Midtown, 224

where most commercial establishments and deliveries take place. Community board groupings 7-225

8 and 9-12 (Uptown) are more residential in nature and attract fewer CMV trips. This calculation 226

assumes that all CMV trips destined for Manhattan are deliveries. 227

For both models, CMV OD trips are stored in period-specific origin-destination (OD) 228

matrices, with each cell containing the number of trips between zones per period. Once the 229

existing commercial vehicle OD matrices are altered, the traffic assignment module of NYBPM 230

and the mesoscopic simulation network is re-run. Alterations to the CMV OD matrices are 231

accomplished with shift factors calculated from the behavioral data, which are applied to CMV 232

OD demands between all originating zones outside of Manhattan and destination zones within 233

Manhattan. The shift factors reduce AM Peak, Midday, and PM Peak period CMV OD trips by a 234

percentage (from the behavioral data), and the sum of the reduced trips is added to the Overnight 235

period, for each OD pair. This has been assumed to be uniformly distributed across the NT hours 236

in the mesoscopic model. Since only food- or retail-related truck traffic is modeled, the estimate 237

of the probability that a commercial vehicle in the traffic stream would be making food or retail 238

deliveries is taken to calculate the percentage of trips in the NYBPM model corresponding to 239

food or retail deliveries. The percentage of commercial vehicle traffic shifting to the off-hours 240

can be, αJ, calculated by Equation 1: 241

∑=e

eJ

eJJ ωρα (1) 242

243 where J = destination zone where receivers are located 244

e = industry segment {retail, food} 245

ρ = percentage of deliveries from industry ‘e’ shifting to off-hours 246

ω = proportion of total deliveries associated with industry ‘e’ 247

248

Computationally, all OD trip shifts are done exogenously in MATLAB. To apply a shit 249

factor to a certain group of zones, all OD pairs with destination in the community board group of 250

J zones being considered receive the shift factor. The qualifying OD pairs are those with the 251

origin open to all zones in the network, Zones 1-4000, and the destination within Manhattan, 252

Zones 1-318, (i,j) = (1:4000,1:318). This signifies that even trips originating within Manhattan 253



are shifted, to account for chained trips, and to maintain the link between ‘deliveries’ from the 254

behavioral data and ‘trips’ in the model. For simplicity, shift factors are used corresponding to 255

the community board groupings described to account for the more than 1,200,000 OD pairs 256

receiving a shift factor. 257

258

Scenarios Modeled 259

For the scenarios simulated in NYBPM the shift factors shown in Table 1 are used. The shift 260

factors are calculated for four community board groupings in Manhattan, and by accounting for 261

both the number of food & retail receivers located in these groupings, and the participation levels 262

predicted by the behavioral studies (12). 263

264



Table 1: Shift Factors by Scenario 265

Scenario Tax Incentive

Community Boards, J

Retail Proportion, ρR

Food Proportion, ρF

Retail Percent, ωR

Food Percent, ωF

Shift Factor, αJ

Average Shift

F1, 2, 3 16.47% 12.83% 3.60%4, 5, 6 21.45% 7.15% 2.57%

7, 8 11.82% 10.11% 2.79%9, 10, 11, 12 14.34% 17.13% 4.46%

1, 2, 3 16.47% 12.83% 8.51%4, 5, 6 21.45% 7.15% 6.02%

7, 8 11.82% 10.11% 6.58%9, 10, 11, 12 14.34% 17.13% 10.56%

1, 2, 3 16.47% 12.83% 12.62%4, 5, 6 21.45% 7.15% 9.29%

7, 8 11.82% 10.11% 9.73%9, 10, 11, 12 14.34% 17.13% 15.44%

1, 2, 3 16.47% 12.83% 15.12%4, 5, 6 21.45% 7.15% 11.70%

7, 8 11.82% 10.11% 11.60%9, 10, 11, 12 14.34% 17.13% 18.14%

1, 2, 3 16.47% 12.83% 17.05%4, 5, 6 21.45% 7.15% 13.97%

7, 8 11.82% 10.11% 13.01%9, 10, 11, 12 14.34% 17.13% 19.98%

1, 2, 3 16.47% 12.83% 23.49%4, 5, 6 21.45% 7.15% 22.28%

7, 8 11.82% 10.11% 17.65%9, 10, 11, 12 14.34% 17.13% 25.65%

22.03%

2.93%

6.90%

10.42%

12.79%

14.83%$25,000 36.39% 86.17%

$50,000 74.80% 87.12%

$15,000 18.39% 74.75%

$20,000 26.71% 83.55%

$5,000 4.59% 22.21%

$10,000 10.44% 52.92%

6

1

2

3

4

5

266 267

The scenarios studied can be interpreted in terms of the average shift factor of each scenario, 268

which is the average of the shift factors for each community board weighted by the proportion of 269

deliveries to each community board grouping. It can be observed that as tax incentive amount 270

increases, the marginal increase in average shift factor decreases, due to a limit in receiver 271

participation. 272

The six scenarios shown are modeled in NYBPM, while the first three are simulated in the 273

extracted mesoscopic sub-network. Prior to this, both models undergo a substantive calibration 274

process using available up-to-date data. The truck OD matrices of NYBPM are inflated to current 275

levels, while the mesoscopic network of Manhattan is calibrated to existing conditions. Both 276

models are refined to represent an accurate base case, since the results of each scenario run are 277

compared with the base-case, and presented in the following section. 278

279



RESULTS 280

NYBPM Results 281

The macroscopic network model’s (NYBPM) assignment output contains information for all 282

53,000 links in the highway network, including vehicle flows by class, travel time, and average 283

speed. Two of the important parameters for measuring traffic effects can be calculated from this 284

output: Vehicle Miles Traveled (VMT) and Vehicle Hours Traveled (VHT). VMT gives an idea 285

on the total distance traveled by all vehicles in the region on a typical day, while VHT is a 286

convenient method of measuring travel times, and by extension, congestion. While changes to 287

VMT do not clearly indicate whether the network is more of less congested, this conclusion can 288

be reached from observing changes to VHT. For example vehicles may take longer paths to 289

avoid congested links, and in turn reducing their overall travel time, thus saving time and 290

reducing VHT while increasing VMT. 291

The results show the net differences between output parameters from the calibrated year 292

2007 base model and the shift scenario model, and percentage changes of the output parameters. 293

First Figure 3a shows the change in vehicle miles traveled (VMT) by a shifting scenario’s 294

assignment on the network, then Figure 3b shows the changes to vehicle hours traveled (VHT). 295

The figures show the resulting output from the entire New York area network of all links in the 296

NYBPM. The total 24-hour day (sum of all four periods) change in vehicle miles traveled (VMT) 297

as a result of a specific scenario’s assignment is represented by the heavy black line, while the 298

gray line represents only the three daytime periods from when truck traffic is subtracted. 299

The full-day results show that as the average shift levels increase vehicle miles traveled and 300

vehicle hours traveled for all vehicles in the network both decrease. However they also show that 301

as the proportion of deliveries shifted increases the marginal benefits decrease. For example 302

beyond a 15% average shift the net benefits are only minimally increased. Figure 4 also shows 303

the change in volume to capacity ratio (v/c), a measure of congestion, for Manhattan roads based 304

on the scenarios of demand shift. Daytime congestion (volume/capacity ratio) in Manhattan is 305

estimated to reduce in excess of 1.5% for significant demand shifts. 306



307

308 Figure 3: NYBPM Network Change in (a) VMT, (b) VHT by Shift Scenario 309

310

(a) Vehicle Miles Traveled (VMT)

(b) Vehicle Hours Traveled (VHT)



The 24-hour net changes in VMT and VHT are aggregated and the net changes from the 311

base-case scenario can be seen in Figure 5a for all network links, and in Figure 5b for changes in 312

Manhattan links. Manhattan, being the target of the program, covers a large proportion of the 313

total network effects and Lower & Midtown Manhattan are the core central business districts of 314

the city. In many scenarios, half of the reduction in VHT is experienced in Manhattan. 315

While the expected benefits from network assignment are expected to resemble the general 316

relationship between tax incentive and average percentage of freight traffic shifted, the exact 317

relationship cannot be followed due to network assignment affects. Particularly, vehicle miles 318

traveled do not always decrease with decreased levels of traffic. Specifically this can be 319

explained as vehicles taking longer paths that are less congested which might still save them time, 320

as they seek to minimize their total trip costs. Vehicle hours traveled however do incrementally 321

decrease with increasing tax incentives and decreased freight traffic in most cases. For some 322

scenario-to-scenario comparisons, reducing the amount of CMVs using the network does not 323

always result in a decrease of vehicle hours traveled. Since NYBPM employs user-equilibrium 324

assignment instead of system-optimal, the effect to the entire system is not always desirable. 325

326 Figure 4: Change in Manhattan Links’ Volume/Capacity Ratios 327



328

329 Figure 5: Scenario Net Benefits: (a) All Network Links, (b) Manhattan Links 330



Mesoscopic Model Results 331

The NYBPM, being a regional macroscopic model, is not largely sensitive to small changes in 332

the number of CMVs assigned during periods. To measure the effects of the OHD program at a 333

more detailed level, a mesoscopic sub-network of Manhattan is extracted and the OHD program 334

modeled. The results are presented with statistics calculated at a path level, where travel times 335

and speeds are computed from the beginning of vehicle trips until vehicles reach their 336

destinations. The results of the focused Manhattan sub-network account for changes in the travel 337

times and speeds for both an individual average trip and for the aggregated effect in total travel 338

times. The results are also aggregated to find the overall effect per day and per period of time 339

(AM, MD, PM, NT), which accounts for network effects in a more consistent fashion. 340

As expected the results show an inverse relationship between percentage shifts and travel 341

times (Figure 6), as well as speeds (Figure 7). ‘Daytime’ aggregates the results for the three peak 342

periods (AM, MD, and PM) in which the truck and other commercial vehicle demands are 343

reduced by the OHD model. These periods show a decrease in total travel time under all 344

scenarios, with the AM period showing the highest decrease in total travel time. The results also 345

show a monotonic increase in travel times for all scenarios, up to 4.2% in Scenario 3 (which 346

represents an average shift of 10.42% of the daytime CMVs) in the NT period. However, this 347

increase in travel time is in all cases outweighed by the reductions in the other daytime periods. 348

This is expected given that the NT period has much less volume than the daytime periods, and 349

the increase is not as significant because the vehicles can move at slightly decreased speed. In 350

particular the effects are largest during the AM Peak and Midday periods, as compared with the 351

reduction in the PM peak, which has a more compact distribution of trips. 352

Figure 8 shows the congestion pattern for the 24 hours of the simulation, and it can be 353

observed that the scenario with the lowest average shift (2.93%) has a similar pattern as the base 354

case (no shift). However, once the shifts are larger (6.90% or 10.42%) the congestion reduces 355

significantly between 7–10 am and between 12–3 pm. During the NT hours (7pm–6am) 356

congestion is increased slightly due to the new traffic added during these hours. The shifts have 357

significant effects during the peak hours of each period, when the traffic is reduced. The overall 358

reduction in total travel times within the Manhattan sub-network over a 24-hour period are 359

0.93% for Scenario 1 (2.93% average shift), 2.93% for Scenario 2 (6.90% average shift), and 360

4.2% for Scenario 3 (10.42% average shift). 361



362

‐10.00%

‐8.00%

‐6.00%

‐4.00%

‐2.00%

0.00%

2.00%

4.00%

6.00%

Scenario 1 (2 93%) Scenario 2 (6 90%) Scenar

Chan

ge from

Base

363

Figure 6: Sub-network Change in Total Travel Time by Period 364 365

‐2.00%

‐1.50%

‐1.00%

‐0.50%

0.00%

0.50%

1.00%

1.50%

2.00%

Daytime NT 24‐hour

Chan

ge from

Base

Scenario 1 (2.93%) Scenario 2 (6.90%) Scenario 3 (10.42%) 366

Figure 7: Sub-network Change in Average Speed per Vehicle for Trips Completed 367



368

Figure 8: Sub-network Travel Time Pattern by Shifting Scenario 369

370 Regional Model/Sub-network Traffic Impact Comparison 371

A comparison between NYBPM and the sub-network simulation results is difficult to perform 372

since the mesoscopic simulation accounts for results and effects within Manhattan, while the 373

NYBPM aggregates the results of the overall regional network. Moreover, there is some 374

accuracy lost due to the reduction of the area of scope in the mesoscopic model. Even if the 375

results of only the Manhattan links of NYBPM are compiled, the output will differ from the sub-376

network links’ results due to the interactions with the neighboring regions’ links in the larger 377

model. In addition the results obtained through the TransModeler simulation are path–based, 378

while the NYBPM provides results at a link level from the traffic assignment nature of the model. 379

In order to perform the comparison TransModeler path-based results obtained are converted into 380

link-based results. Using the information from paths, the total travel times in segments 381

corresponding to the links of the NYBPM are obtained. These results per link are aggregated by 382

time period and presented in Figure 9, compared with NYBPM results for the three scenarios 383

(average shifts of 2.93%, 6.90%, and 10.42%) run in both models. 384

It can be observed that the mesoscopic simulation shows far greater travel time savings 385

during the daytime than the NYBPM. These differences are also reflected in the 24-hour results 386



for all scenarios, where the mesoscopic simulation is observed to provide larger changes, 387

percentage-wise, compared with NYBPM. The results indicate that the mesoscopic sub-network 388

is more sensitive to the reduction in daytime truck traffic than the NYBPM. These differences are 389

due to how each model manages congestion and the traffic flow model used. For instance a 390

macro-model, such as NYBPM, uses a simplified traffic flow model, while the mesoscopic 391

simulator uses a more sophisticated traffic model and accounts for more realistic ways to 392

compute and aggregate delays. During the NT period, this difference is not significant because 393

the period does not have significant congestion. However the daytime periods have significant 394

congestion, which causes differences in the model output. For the sake of completeness the 395

results of the path-based results were included, and it can be observed that in general the link-396

based results of the simulations slightly overestimate the travel times saved. Overall the 397

mesoscopic model is seen to be more sensitive to the OHD program studied, while the NYBPM, 398

being a large-scale model, is not as sensitive. 399

400

401 Figure 9: NYBPM-Sub-network Comparison (Manhattan Links) 402

403

CONCLUSION 404

This paper presents the results of modeling an off-hour delivery program for New York City in 405



two traffic modeling tools. Both networks are calibrated to the needs of this study and behavioral 406

modules representing the behavior of freight carriers responding shifting their deliveries to 407

overnight hours were implemented following the methodology. Based on the response to certain 408

tax incentives by businesses within Manhattan, commercial vehicles providing deliveries to them 409

shift their trips to the overnight period. Both a macroscopic regional travel demand model and a 410

mesoscopic sub-simulation network show a measurable impact to congestion and network 411

conditions. During the daytime hours, as trips are shifted away, travel times improve and link 412

speeds increase; while in the overnight period conditions deteriorate due to additional 413

commercial vehicle trips. However the benefits (in terms of travel time) observed during the 414

daytime hours in both modeling tools are greater than the losses to network efficiency observed 415

in the night hours. Therefore the OHD program is projected to have a net positive effect on the 416

traffic network. 417

Since both tools provide different type of results, the path based data obtained from the 418

mesoscopic simulation model is converted to link based data in order to compare the results of 419

both models. In general terms, the overnight period, where traffic is accommodated after the shift, 420

provides similar results. Differences arise in the daytime period, which are periods with 421

significant congestion. In these cases the mesoscopic model is more sensitive to the reduction in 422

the traffic congestion. This difference is expected because static models, such as NYBPM, do not 423

completely accounts for congestion effects in traffic. The mesoscopic simulation is more 424

appropriate for studying the detailed effects of traffic changes on vehicles, but extension to the 425

full regional network is impractical due to the simulation time. The usage of both models allows 426

for studying the OHD program’s effects on a large area while still understand the maximum 427

beneficial effects at a detailed, mesoscopic scale. 428

429

ACKNOWLEDGEMENTS 430

The authors would like to acknowledge USDOT for funding this innovative research study, as 431

well as New York/New Jersey-area transportation agencies such as NYMTC, NYSDOT, 432

NYCDOT, NJDOT, NJTA, and PANYNJ for providing data and models used in this study. Their 433

support is appreciated. 434

435

436



REFERENCES 437

1. Crainic, T. G., Ricciardi, N., & Storchi, G. (2004). Advanced Freight Transportation Systems 438

for Congested Urban Areas. Transportation Research Part C, 12, 119-137. 439

2. Ioannou, P., Anastasios, C., Bose, A., Jula, H., Kanaris, A., Pourhammadi, H., et al. (2001). 440

Modeling and Route Guidance of Trucks in Metropolitan Areas. Metrans Transportation 441

Center. 442

3. Holguin-Veras, J., Perez, N., Cruz, B., & Polimeni, J. (2006). Effectiveness of Financial 443

Incentives for Off-Peak Deliveries to Restaurants in Manhattan, New York. Transportation 444

Research Record: Journal of the Transportation Research Board, 1966, 51-59. 445

4. Holguin-Veras, J., List, G. F., Meyburg, A. H., Ozbay, K., Paaswell, R. E., Teng, H., et al. 446

(2001). An Assessment of Methodological Alternatives for a Regional Freight Model in the 447

NYMTC Region. New York: Region II University Transportation Research Center. 448

5. Han, L. D., Chin, S.-M., Franzese, O., & Hwang, H. (2005). Estimating the Impact of 449

Pickup- and Delivery-Related Illegal Parking Activities on Traffic. Transportation Research 450

Record: Journal of the Transportation Research Board , 1906, 49-55. 451

6. Yannis, G., Golias, J., & Antoniou, C. (2006). Effects of Urban Delivery Restrictions on 452

Traffic Movements. Transportation Planning and Technology , 29 (4), 295-311. 453

7. “Traffic Analysis Toolbox” (2004). Federal Highway Administration. 454

8. Parsons Brinckerhoff Quade & Douglas, Inc. (2005). Transportation Models and Data 455

Initiative: New York Best Practice Model (NYBPM). New York Metropolitan Council. 456

9. “New York Best Practice Model Final Report” (2005). New York Metropolitan Council 457

10. “2005-2030 Regional Transportation Plan” (2005). New York Metropolitan Transportation 458

Council. 459

11. “TransModeler User’s Guide” (2009). Caliper Corportation 460

12. Silas, M.A. & Holguin-Veras, J. (2009). A Behavioral Micro-Simulaion for the Analysis of 461

Off-Hour Delivery Prolicies. Transportation Research Board Annual Meeting, Washignton , 462

DC. 463

13. Holguin-Veras, J,R., Silas, M.A, et al. (2006). An Investigation on the Effectiveness of Joint 464

Receiver-Carrier Policies to Increase Truck Traffci in the Off-Peak Hours: Part I: The 465

Behavior of Receivers.” Networks and Spatial Economics. 466



14. Holguin-Veras, J,R., Silas, M.A, et al. (2006). An Investigation on the Effectiveness of Joint 467

Receiver-Carrier Policies to Increase Truck Traffci in the Off-Peak Hours: Part II: The 468

Behavior of Carriers.” Networks and Spatial Economics. 469


traffic impact analysis v2

Documents