Integrating Urban Last-Mile Package Deliveries into an Agent-Based Travel Demand Model

Every sector needs to minimize GHG emissions to limit climate change. Emissions from transport, however, have remained mostly unchanged over the past thirty years. In particular, air travel for short-haul flights is a significant contributor to transport emissions. This article identifies factors that influence the demand for domestic air travel. An agent-based model was implemented for domestic travel in Germany to test policies that could be implemented to reduce air travel and CO2 emissions. The agent-based long-distance travel demand model is composed of trip generation, destination choice, mode choice and CO2 emission modules. The travel demand model was estimated and calibrated with the German Household Travel Survey, including socio-demographic characteristics and area type. Long-distance trips were differentiated by trip type (daytrip, overnight trip), trip purpose (business, leisure, private) and mode (auto, air, long-distance rail and long-distance bus). Emission factors by mode were used to calculate CO2 emissions. Potential strategies and policies to reduce air travel demand and its CO2 emissions are tested using this model. An increase in airfares reduced the number of air trips and reduced transport emissions. Even stronger effects were found with a policy that restricts air travel to trips that are longer than a certain threshold distance. While such policies might be difficult to implement politically, restricting air travel has the potential to reduce total CO2 emissions from transport by 7.5%.

Download Full-text

Determining service provider and transport system related effects of ridesourcing services by simulation within the travel demand model mobiTopp

European Transport Research Review ◽

10.1186/s12544-021-00493-3 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Gabriel Wilkes ◽

Lars Briem ◽

Michael Heilig ◽

Tim Hilgert ◽

Martin Kagerbauer ◽

...

Keyword(s):

Transport System ◽

Travel Demand ◽

Waiting Times ◽

Demand Model ◽

Agent Based ◽

Demand Models ◽

Decision Making Processes ◽

The City ◽

As Effects ◽

Travel Demand Model

Abstract Purpose Ridesourcing services have become popular recently and play a crucial role in Mobility as a Service (MaaS) offers. With their increasing importance, the need arises to integrate them into travel demand models to investigate transport system-related effects. As strong interdependencies between different people’s choices exist, microscopic and agent-based model approaches are especially suitable for their simulation. Method This paper presents the integration of shared and non-shared ridesourcing services (i.e., ride-hailing and ride-pooling) into the agent-based travel demand model mobiTopp. We include a simple vehicle allocation and fleet control component and extend the mode choice by the ridesourcing service. Thus, ridesourcing is integrated into the decision-making processes on an agent’s level, based on the system’s specific current performance, considering current waiting times and detours, among other data. Results and Discussion In this paper, we analyze the results concerning provider-related figures such as the number of bookings, trip times, and occupation rates, as well as effects on other travel modes. We performed simulation runs in an exemplary scenario with several variations with up to 1600 vehicles for the city of Stuttgart, Germany. This extension for mobiTopp provides insights into interdependencies between ridesourcing services and other travel modes and may help design and regulate ridesourcing services.

Download Full-text

ADDING A NEW STEP WITH SPATIAL AUTOCORRELATION TO IMPROVE THE FOUR-STEP TRAVEL DEMAND MODEL WITH FEEDBACK FOR A DEVELOPING CITY

IATSS Research ◽

10.1016/s0386-1112(14)60236-3 ◽

2009 ◽

Vol 33 (1) ◽

pp. 44-54 ◽

Cited By ~ 4

Author(s):

Xuesong FENG ◽

Junyi ZHANG ◽

Akimasa FUJIWARA

Keyword(s):

Spatial Autocorrelation ◽

Travel Demand ◽

Demand Model ◽

Travel Demand Model

Download Full-text

Model Calibration and Forecasts of Air Travel Demand with Categorized Household Socioeconomic Attributes

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198120919111 ◽

2020 ◽

Vol 2674 (6) ◽

pp. 363-371

Author(s):

Jungin Kim ◽

Ikki Kim ◽

Jaeyeob Shim ◽

Hansol Yoo ◽

Sangjun Park

Keyword(s):

Travel Behavior ◽

Household Income ◽

Travel Demand ◽

Behavioral Model ◽

Income Group ◽

Air Travel ◽

Jeju Island ◽

Demand Model ◽

Air Travel Demand ◽

Travel Demand Model

The objectives of this study were to (1) construct an air demand model based on household data and (2) forecast future air demand to explain the relationship between air demand and individual travel behavior. To this end, domestic passenger air travel demand at Jeju Island in South Korea was examined. A multiple regression model with numerous explanatory variables was established by examining categorized household socioeconomic data that affected air demand. The air travel demand model was calibrated for 2009–2015 based on the annual average number of visits to Jeju Island by households in certain income groups. The explanatory variable was set using a dummy variable for each household income group and the proportion of airfare to GDP per capita. Higher household income meant more frequent visits to Jeju Island, which was well-represented in the model. However, the value of the coefficient for the highest income was lower than the value for the second-highest income group. This suggested that the highest income group preferred overseas travel destinations to domestic ones. The future air demand for Jeju airport was predicted as 26,587,407 passengers in 2026, with a subsequent gradual increase to approximately 33,000,000 passengers by 2045 in this study. This study proposed an air travel demand model incorporating household socioeconomic attributes to reflect individual travel behavior, which contrasts with previous studies that used aggregate data. By constructing an air travel model that incorporated socioeconomic factors as a behavioral model, more accurate and consistent projections could be obtained.

Download Full-text