Understanding social processes of shopping destination choice - An approach to model stability and variability

Transport systems are affected by fundamental technical and social dynamics. For planners and researchers, travel demand models are an important tool to gain insights into possible effects of these dynamics and to find appropriateways of dealing with them. However, most state-of-the-art travel demand models underestimate social aspects of travel choices, which we consider essential for understanding stability and variability of travel behavior. Based on a qualitative interview study, the paper presents an interdisciplinary approach to consider social aspects for modeling shopping destination choice. Starting point for our considerations is that people are social beings, moving around to build and maintain relationships, and that these relationships only unfold in relation to overall sociotechnical structures. The interview study provides evidence for relationships between stores and customers. Relationships can be distinguished in two dimensions. First, in terms of the nature of a relationship: having a relationship either with the owner of a specific store or towards specific brands. Second, in terms of the meaning: if the relationships is perceived meaningfull or obligatory. Findings are represented in a first modeling approach. The results show that the more seldom relationships to owners of a store can be modeled easily, while the more typical relationships to brands require further research

Integrating Neighbours into an Agent-Based Travel Demand Model to Analyse Success Rates of Parcel Deliveries

The rapid growth of the e-commerce market leads us to expect a further increase in delivery vehicles in urban areas as well. This growth is expected to be accompanied by an increase in emissions while space becomes scarce. Meanwhile, people are adjusting their travel behaviour; therefore, the growing e-commerce market affects both last-mile delivery and private passenger traffic. Failed home deliveries are an important factor. They produce additional traffic by both ”Courier, Express and Parcel” (CEP) service providers and private passengers in the form of repeated delivery attempts or trips to pick up parcels. In this paper we apply an integrated agent-based model of last-mile deliveries and private travel demand. This allows for analysis of interactions between delivery and private passenger traffic and the status of the recipients during delivery. Furthermore, we present a neighbourship model to account for deliveries accepted by neighbours, which is crucial to reproduce realistic delivery success rates. We applied the presented model to the city of Karlsruhe, Germany, and simulated multiple delivery policy scenarios, which we compare to a static model without interactions between private and delivery agents. Our results show that the agent-based model produces more nuanced success rates with respect to different socio-demographic groups. Differentiating these groups is necessary when assessing measures that target specific groups and analysing effects of demographic changes. Also, we show the necessity of considering neighbours in such a model. This paper provides insight into the effects of e-commerce on a transport system and a framework to analyse policy measures or alternative delivery strategies

Dynamics and Processes in Operations Control Centers in Urban Public Transport: Potentials for Improvement

Disruptions in public transport operations occur every day. Thus, providing a reliable system is a challenge for operations and planning. This paper gives insights into the dynamics and processes of operations control centers in public transport to reveal potentials for further improvement in reliability. Therefore, directors were interviewed, dispatchers observed, and operations documentation was studied. It has become obvious that the process of dispatching has four different types of call signals (assault, accident, missing replacement, and wish-to-talk) corresponding to different kinds of incidents. The drivers use those call signals to contact the operations control center and initialize different procedures of communication between the dispatchers, drivers, and other involved parties. As the communication is mostly conducted via phone or radio, several improvements are possible, such as training in communications and increased use of information technology in operations. In planning tools, the handling of incidents is marginally supported. As all kinds of incidents can affect the service, they should be represented in planning tools to design more reliable public transport systems. However, they do not need to be represented in full detail. Verbal communication could mostly be reduced to single decisions. Accidents, for example, influence the operation by delayed vehicles and blocked ways. The findings of this work allow a better understanding of operations control centers and reveal their potentials for improvement

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

Integrating urban last-mile package deliveries into an agent-based travel demand model

With the expected increase of e-commerce activity, we can expect the share of delivery vehicles in cities to rise as well. On the one hand, this puts great pressure on cities and surrounding areas as emissions rise and space becomes scarce. On the other hand, people are adjusting their travel behaviour such that the increase in e-commerce affects not only last-mile delivery but also private passenger traffic. This paper presents an integrated approach of modelling last-mile deliveries using an agent-based travel demand model. It is intended to account for reciprocal effects between online shopping behaviour and last-mile deliveries. The package orders are generated by agents in the study area and distributed among the package centres. For each package centre, the tour for each delivery agent is created. The presented model allows for the simultaneous simulation of private trips and last-mile deliveries and thus realistic delivery conditions: the model can detect e.g. if an agent or another household member is at home to receive their order. We have applied the model to the city of Karlsruhe, Germany, and describe first results of that simulation. Application of the model allows for a detailed analysis e.g. of delivery success rates both in terms of time and space. The presented modelling framework provides insight into effects of last-mile deliveries on a transportation system and can be availed to analyse policy measures or alternative delivery strategies

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

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

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

Self-Regulating Demand and Supply Equilibrium in Joint Simulation of Travel Demand and a Ride-Pooling Service

This paper presents the coupling of a state-of-the-art ride-pooling fleet simulation package with the mobiTopp travel demand modeling framework. The coupling of both models enables a detailed agent- and activity-based demand model, in which travelers have the option to use ride-pooling based on real-time offers of an optimized ride-pooling operation. On the one hand, this approach allows the application of detailed mode-choice models based on agent-level attributes coming from mobiTopp functionalities. On the other hand, existing state-of-the-art ride-pooling optimization can be applied to utilize the full potential of ride-pooling. The introduced interface allows mode choice based on real-time fleet information and thereby does not require multiple iterations per simulated day to achieve a balance of ride-pooling demand and supply. The introduced methodology is applied to a case study of an example model where in total approximately 70,000 trips are performed. Simulations with a simplified mode-choice model with varying fleet size (0–150 vehicles), fares, and further fleet operators’ settings show that (i) ride-pooling can be a very attractive alternative to existing modes and (ii) the fare model can affect the mode shifts to ride-pooling. Depending on the scenario, the mode share of ride-pooling is between 7.6% and 16.8% and the average distance-weighed occupancy of the ride-pooling fleet varies between 0.75 and 1.17