Influences of Norm and Excitement on Bike Use Behavior of High-Income People in China

In China, the bicycle had a high relevance in the past. Some decades ago, it was the major mode for most Chinese people. This situation changed with growing wealth and increasing car ownership. Today, as cities are traffic-crowded, the bicycle seems to be an alternative again. At the same time, the government as well as private equity, invest in public bike systems. Previous research indicates that especially people with high-income are less likely to use the bicycle as a mode of transport. The question arises whether the bike is used by high-income people that usually have a car as an alternative. What are the influencing factors to use bicycles? To investigate these aspects, we present results of a study conducted in 8 Chinese cities. The data is analyzed using a structural equation model to investigate influences on bike use behavior of high-income people. This study provides no contribution in the research of psychological characteristics of users or the routes used. Rather, it is intended to provide understanding to ecological norm and excitement regarding usage. The results provides insights into the complex interrelationships of sociodemographic and psychological aspects as well as the modernity of the cities in the context of bike usage. Mainly car ownership and the place of residence show significant effects on the attitudes and norms of people and thus influence the use of bicycles. Our results help to understand the interrelationships between sociodemographic characteristics, spatial characteristics and the attitudes of people while making mobility decisions

How does the setup of sample collection influence survey results - an example of new mobility services

Conducting surveys in transportation research is becoming more complex. Depending on the survey subject, the survey format and the circumstances of the sample collection the motivation of respondents to participate and consequently the results can vary substantially. Skewness of samples and sample selection bias occur to different degrees. This study compares different surveys which were created to capture the acceptance regarding shared autonomous minibuses in Germany. By analyzing distributions of behavior, perception and intention to use the services, biases in the datasets are worked out. The results show that voluntary on-site surveys lead to more positive perception of minibuses

Modeling intermodal travel behavior in an agent-based travel demand model

The topic of intermodal passenger mobility has become more important during the last 20 years. As mobility options increase in number and flexibility, it gets more and more attractive to combine multiple modes on single trips. In addition, intermodal travel behavior is expected to contribute to less car dependent mobility and transport sector’s reduction of greenhouse gas emissions. Creating and improving the conditions for such a behavior requires planning with knowledge about influencing factors and highest resistances. Empirical evidence and behavioral models can support decisions on measures improving intermodal travel supply. This work presents an agent-based model approach containing intermodal travel behavior with regard to its most important decisions. It enables the combination of a multitude of modes and can be extended to even more modes. By combining many decisions and influences it is comprehensible and adaptable to different surveys and circumstances. We show that results are realistic and impacts are valid to be able to forecast effects of potential measures

Using OpenStreetMap as a Data Source for Attractiveness in Travel Demand Models

We present a methodology to extract points of interest (POIs) data from OpenStreetMap (OSM) for application in travel demand models. We use custom taglists to identify and assign POI elements to typical activities used in travel demand models. We then compare the extracted OSM data with official sources and point out that the OSM data quality depends on the type of POI and that it generally matches the quality of official sources. It can therefore be used in travel demand models. However, we recommend that plausibility checks should be done to ensure a certain quality. Further, we present a methodology for calculating attractiveness measures for typical activities from single POIs and national trip generation guidelines. We show that the quality of these calculated measures is good enough for them to be used in travel demand models. Using our approach, therefore, allows the quick, automated, and flexible generation of attractiveness measures for travel demand models

Quality Assessment of OpenStreetMap’s Points of Interest with Large-Scale Real Data

OpenStreetMap (OSM) data are geographical data that are easy and open to access and therefore used for a large set of applications including travel demand modeling. However, often there is a limited awareness about the shortcomings of volunteered geographic information data, such as OSM. One important issue for the application in travel demand modeling is the completeness of OSM elements, particularly points of interest (POI), since it directly influences the predictions of trip distributions. This might cause unreliable model sensitivities and end up in wrong predictions leading to expensive misinterpretations of the effects of policy measures. Because of a lack of large-scale real-world data, a detailed assessment of the quality of POI from OSM has not been done yet. Therefore, in this work, we assess the quality of POI from OSM for use within travel demand models using surveyed real-world data from 49 areas in Germany. We perform a descriptive and a model-based analysis using spatial, demographic, and intrinsic indicators for two common trip purpose categories used in travel demand modeling. We show that the completeness of POI data in OSM depends on the category of POI. We further show that intrinsic indicators and indicators calculated based on data from other sources (e.g., land use or census data) are able to detect quality deficiencies of OSM data

On the Variation in Mode Choice Behavior in Agent-based Travel Demand Models

Past research shows that individual mode choice preferences and, thus, taste variation of mode choice play an essential role and are observable even for one day. Both multiand monomodal behavioral patterns with different degrees of mode choice variation are the subject of investigation. Hence, agent-based travel demand models (AB-TDMs) must account for this taste variation, which is expected to affect model sensitivity. To assess the impacts of mode choice model configuration on the resulting variation, we apply an approach based on mixed logit models and implement them in an AB-TDM simulation. We analyze the mode choice behavior regarding variation indicators for the simulation period of one day and one week and compare it to observed behavior. We show that classic MNL models cannot appropriately account for mode choice variation in AB-TDM, both for one week or one day. We show that mixed model approaches can bridge this gap by better capturing heterogeneity and suggest using mixed models in an agent-based context. This prevents models from overestimating multimodal mode choice behavior