Representation of Work-Related Trip Patterns in Household and Commercial Travel Surveys

This paper considers which work-related trip patterns are included in household travel surveys and which in commercial travel surveys and if there are certain patterns that are distinctly underrepresented in either one. The study is structured as a comparison between data from a household travel survey and data from a commercial travel survey. Both surveys were conducted in Germany and within close temporal proximity. We applied cluster analysis to identify differences in the data and identify work-related travel patterns. The results show that work-related travel patterns are quite complex. Although some patterns are covered in both surveys, mobile workers’ travel patterns in particular are not represented well in the household travel survey. Furthermore, our analysis shows that not all commercial trips are generated by motorized vehicles and a considerable share of work-related trips are undertaken using public transport or active modes of transport that are not covered by the commercial travel survey. The results indicate that researchers and transport planners creating travel demand models need to pay more attention to work-related travel behavior and acknowledge that depending on the area of study, traditional household travel surveys may not provide a complete sample of the population; however, simply adding data on commercial trips from commercial travel demand models to data from household travel surveys does not provide a complete picture of work-related travel either

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

Effects of COVID-19 on Telework and Commuting Behavior: Evidence from 3 Years of Panel Data

The COVID-19 pandemic has forced employers and employees to re-evaluate their attitudes toward telecommuting. This induced a change in the sheer number of people who have started to work from home (WFH). While previous studies highlight differences between telecommuters based on their level of telecommuting experience, these effects have not been studied in detail. This may limit the evaluation of implications for post-pandemic times and the transferability of models and predictions based on data collected during the COVID-19 pandemic. This study expands on previous findings by comparing the characteristics and behavior of those who have started to telecommute during the pandemic and those who had already telecommuted before. Furthermore, this study addresses the uncertainty that exists about whether the findings of studies conducted before the pandemic—for example about sociodemographic characteristics of telecommuters—still hold true, or if the pandemic induced a shift in telecommuters’ profiles. Telecommuters show differences when considering their previous experience in WFH. The results of this study suggest that the transition induced by the pandemic was more drastic for new telecommuters compared with experienced telecommuters. The COVID-19 pandemic had an effect on how household configurations are considered in the choice to WFH. With decreased access to child care resulting from school closings, people with children in the household were more likely to choose to telecommute during the pandemic. Also, while people living alone are generally less likely to choose to WFH, this effect was reduced as a result of the pandemic

Representation of Work-Related Trip Patterns in Household and Commercial Travel Surveys

This paper considers which work-related trip patterns are included in household travel surveys and which in commercial travel surveys and if there are certain patterns that are distinctly underrepresented in either one. The study is structured as a comparison between data from a household travel survey and data from a commercial travel survey. Both surveys were conducted in Germany and within close temporal proximity. We applied cluster analysis to identify differences in the data and identify work-related travel patterns. The results show that work-related travel patterns are quite complex. Although some patterns are covered in both surveys, mobile workers’ travel patterns in particular are not represented well in the household travel survey. Furthermore, our analysis shows that not all commercial trips are generated by motorized vehicles and a considerable share of work-related trips are undertaken using public transport or active modes of transport that are not covered by the commercial travel survey. The results indicate that researchers and transport planners creating travel demand models need to pay more attention to work-related travel behavior and acknowledge that depending on the area of study, traditional household travel surveys may not provide a complete sample of the population; however, simply adding data on commercial trips from commercial travel demand models to data from household travel surveys does not provide a complete picture of work-related travel either

Microscopic Demand Modeling of Urban and Regional Commercial Transport

Commercial transport is an intrinsic part of the evaluation of traffic volumes. However, it is often limited to freight transport, and while this is a significant element, it disregards the share of trips contributed by plumbers, electricians, care services, and the like. These businesses add a significant part to the commercial traffic volume, especially in urban areas. The reasons, commercial passenger transport lacks behind are wide-ranging, one of the leading causes being difficulties in gathering sufficient data. In this paper, we present a microscopic approach to model commercial travel demand, including but not limited to freight traffic, based on data from a national survey and open data. We differentiate between vehicles of businesses that have a fixed daily schedule, with only small variations of their trip purposes and vehicles of businesses that can predict their daily schedules only to a certain degree. The latter have varying trip purposes and decide on a short-term base if and what sort of trip is to be pursued. Vehicles with fixed daily schedules include plumbers, electricians, care services, and delivery trucks. Due to our database, we produced a model for these vehicles exemplary for delivery by determining the number of trips for a day and assigning destinations to those trips afterward. We also take the number of private trips into account, laying the foundation of being able to incorporate the commercial transport model into a passenger transport model. We show that our model can overcome the lack of regional data. Based on generic data, the application of our approach shows promising results for the urban and regional commercial travel demand of a model region. By basing our model on generic data, we introduced an opportunity to model commercial travel demand not only in one model region but also for other urban areas in Germany and possibly in various areas in Europe, assuming that structural data is similar

Agent-based model of last-mile parcel deliveries and travel demand incorporating online shopping behavior

In this paper, we present an extension of the agent-based travel demand model mobiTopp with a last-mile parcel delivery module called logiTopp, in which online shopping choice is modeled explicitly. Online shopping behavior is modeled using logistic and Poisson regression models, which consider both the socio-demographic characteristics of the customer and aspects of their travel behavior. As mobiTopp is a framework that simulates travel demand over one week, we are able to capture interactions between travel behavior and online shopping that do not become apparent in single-day simulations. The results show that the integrated choice model reflects the findings presented in the literature in that male, affluent, young professionals are most likely to (frequently) order parcels online compared to other groups of the population. Application of the agent-based model to a city in Germany shows that socio-demographic and behavioral characteristics are considered realistically within the simulation. The model presented here is a suitable simulation tool for alternative urban last-mile delivery solutions, and the open-source and modular framework allows for transfer to other regions as the underlying choice models are consistent with literature from other spatial contexts. The findings are of interest to transportation planners and policymakers as they contribute to the understanding of how increased e-commerce demand influences the transportation system and solutions to mitigate adverse effects

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

Auswirkungen von COVID-19 auf das Arbeiten von Zuhause – eine Analyse auf Basis der Daten des Deutschen Mobilitätpanels

In diesem Paper wird eine Analyse der Auswirkungen von Home Office und der Einflüsse auf die Entscheidung, von zu Hause aus zu arbeiten vorgestellt. Dank des Paneldesigns liefert das Deutsche Mobilitätspanel einzigartige Daten von Personen, die vor und während der COVID-19-Pandemie teilgenommen haben. Unsere Ergebnisse zeigen, dass die die Pandemie das Pendelverhalten stark beeinflusst hat und die Veränderungen teilweise auch zukünftig bleiben werden

Analysing Long-Term Effects of the Covid-19 Pandemic on Last-Mile Delivery Traffic Using an Agent-Based Travel Demand Model

E-commerce demand has increased steadily over the last decades and this trend has accelerated even more since the start of the Covid-19 pandemic. This entailed that user groups such as older people who previously only shopped in-store were incited to shop online to reduce risk of infection leading some to switch to online shopping as the main shopping channel. This study analyses the long-term effects of increased online shopping and subsequent delivery demand due to the Covid-19 pandemic using an agent-based travel demand model. We analyse the simulation of two scenarios for the model area Karlsruhe, Germany: one scenario simulates the parcel delivery demand before the pandemic and the other scenario simulates the demand during the pandemic of the synthetic population. Our results show that there have been shifts in both socio-demographic characteristics of online shoppers and spatial distribution of parcel delivery demand induced by the Covid-19 pandemic. The scenario simulation based on the pandemic related data shows that not only the influence of income has shifted but also the effects of age on e-commerce activity has changed due to the pandemic. The findings are of interest to transport planners and delivery service providers as they highlight the importance of recognising that the Covid-19 pandemic not only induced a shift in socio-demographic profiles of online shoppers but that this shift also entails a change in the spatial distribution of parcel deliveries

The Effect of Agglomeration on Arsenic Adsorption Using Iron Oxide Nanoparticles

The presence of arsenic in groundwater and other drinking water sources presents a notable public health concern. Although the utilization of iron oxide nanomaterials as arsenic adsorbents has shown promising results in batch experiments, few have succeeded in using nanomaterials in filter setups. In this study, the performance of nanomaterials, supported on sand, was first compared for arsenic adsorption by conducting continuous flow experiments. Iron oxide nanoparticles (IONPs) were prepared with different synthetic methodologies to control the degree of agglomeration. IONPs were prepared by thermal decomposition or coprecipitation and compared with commercially available IONPs. Electron microscopy was used to characterize the degree of agglomeration of the pristine materials after deposition onto the sand. The column experiments showed that IONPs that presented less agglomeration and were well dispersed over the sand had a tendency to be released during water treatment. To overcome this implementation challenge, we proposed the use of clusters of iron oxide nanoparticles (cIONPs), synthesized by a solvothermal methodology, which was explored. An isotherm experiment was also conducted to determine the arsenic adsorption capacities of the iron oxide nanomaterials. cIONPs showed higher adsorption capacities (121.4 mg/g) than the other IONPs (11.1, 6.6, and 0.6 mg/g for thermal decomposition, coprecipitation, and commercially available IONPs, respectively), without the implementation issues presented by IONPs. Our results show that the use of clusters of nanoparticles of other compositions opens up the possibilities for multiple water remediation applications