Sebastiaan Thoen
Please Note
16 records found
1
MASS-GT
An empirical model for the simulation of freight policies
MASS-GT
An empirical model for the simulation of freight policies
E-commerce is a rapidly developing segment within urban goods transportation. While currently making up only 5.5% of all kilometers driven by vans in the Netherlands (CBS, 2018), this segment is experiencing rapid growth. For example, in the Netherlands, the volume of parcel deliveries grew by 20% in 2018 (ACM, 2018). This growth is further accelerated by the current coronavirus pandemic. The increasing importance of this segment is not reflected yet in strategic models used in practice for transportation demand forecasting. Parcel deliveries are not modelled explicitly, which does not only prevent the model users from calculating effects of scenarios and policies related to e-commerce, but it also disregards the unique nature of this segment (e.g. specific depot locations, strong growth rates) and, therefore, leads to flawed reference forecasts of van traffic.
In the scientific literature there are some first examples of simulation models for parcel deliveries (Sakai et al, 2020; Hörl & Puchinger, 2021; Llorca & Moeckel, 2021; Mommens et al., 2021; Reiffer et al., 2021). However, each of these examples lacks an empirical disaggregate demand model, focusses on one (part of a) city rather than a whole region, or focusses on one product/service segment rather than all parcel deliveries in a region. Moreover, little experience is gathered in applying such urban freight simulators for traffic forecasting.
2. Methodology, results and main contributions
To combat these shortcomings of the state-of-practice freight models, we developed a module for last-mile parcel deliveries and used it to explore the impacts of different scenario assumptions regarding parcel demand and scheduling. The module consists of two sub-modules: the parcel demand sub-module and the parcel scheduling sub-module.
The parcel demand sub-module calculates parcel demand based on the households and businesses in each zone. For parcels to households (B2C), a logit model is estimated on the Mobility Panel Netherlands, which included several questions regarding parcel orders in 2017 (Hoogendoorn-Lanser et al., 2015). For parcels to businesses (B2B) an average factor is deduced from aggregate statistics reported by ACM (2018). Once the total number of parcels is determined, they are spread over the different courier companies, based on their respective market shares in terms of number of parcels. Finally, for each zone and courier, the nearest depot is determined; from this depot the parcels will be shipped to their end destinations.
The parcel scheduling sub-module forms round-tours to deliver all the parcels for each parcel courier in the study area. From each depot, simple heuristics (such as nearest-neighbor and 2-opt) are used to form efficient tours that respect the vehicle capacity (in terms of number of parcels) and maximum shift lengths for drivers. This, in turn, leads to trip matrices that can be assigned to the network to arrive at network statistics such as vehicle kilometers and emissions.
The conceptual architecture of this module has been applied in four modelling systems:
(1) the strategic freight model of Flanders (SVRM);
(2) the travel demand model of the Municipality of Amsterdam (VMA);
(3) the module for logistical decision-making of the national strategic freight model of the Netherlands (BasGoed);
(4) and the tactical freight simulator of the HARMONY project for the European Commission (test bed: Province of Zuid-Holland, the Netherlands).
In this research we will analyze the impacts of different scenarios and policies, for this purpose we will use the HARMONY implementation of the model in Zuid-Holland. The scenarios explore the impacts of different developments for:
• increased demand for parcels;
• horizontal collaboration between couriers, with shared use of depots;
• a zero-emission zone in Rotterdam, in combination with consolidation centers at the outskirts of the city.
3. Conclusion and future works
A disaggregate region-wide simulation model for parcel deliveries is necessary to evaluate the impacts of policies and developments in e-commerce. Using the developed model, we can show, for example, that vehicle kilometers do not increase linearly with parcel demand due to increased consolidation and that the impacts of zero-emission zones can be diffuse due to rerouting of van trips.
Future efforts may focus on modelling the whole transport chain of e-commerce, rather than only the last-mile deliveries. Furthermore, a demand model for parcels to businesses in line with the model for households is desired, this would require additional data collection.
...
E-commerce is a rapidly developing segment within urban goods transportation. While currently making up only 5.5% of all kilometers driven by vans in the Netherlands (CBS, 2018), this segment is experiencing rapid growth. For example, in the Netherlands, the volume of parcel deliveries grew by 20% in 2018 (ACM, 2018). This growth is further accelerated by the current coronavirus pandemic. The increasing importance of this segment is not reflected yet in strategic models used in practice for transportation demand forecasting. Parcel deliveries are not modelled explicitly, which does not only prevent the model users from calculating effects of scenarios and policies related to e-commerce, but it also disregards the unique nature of this segment (e.g. specific depot locations, strong growth rates) and, therefore, leads to flawed reference forecasts of van traffic.
In the scientific literature there are some first examples of simulation models for parcel deliveries (Sakai et al, 2020; Hörl & Puchinger, 2021; Llorca & Moeckel, 2021; Mommens et al., 2021; Reiffer et al., 2021). However, each of these examples lacks an empirical disaggregate demand model, focusses on one (part of a) city rather than a whole region, or focusses on one product/service segment rather than all parcel deliveries in a region. Moreover, little experience is gathered in applying such urban freight simulators for traffic forecasting.
2. Methodology, results and main contributions
To combat these shortcomings of the state-of-practice freight models, we developed a module for last-mile parcel deliveries and used it to explore the impacts of different scenario assumptions regarding parcel demand and scheduling. The module consists of two sub-modules: the parcel demand sub-module and the parcel scheduling sub-module.
The parcel demand sub-module calculates parcel demand based on the households and businesses in each zone. For parcels to households (B2C), a logit model is estimated on the Mobility Panel Netherlands, which included several questions regarding parcel orders in 2017 (Hoogendoorn-Lanser et al., 2015). For parcels to businesses (B2B) an average factor is deduced from aggregate statistics reported by ACM (2018). Once the total number of parcels is determined, they are spread over the different courier companies, based on their respective market shares in terms of number of parcels. Finally, for each zone and courier, the nearest depot is determined; from this depot the parcels will be shipped to their end destinations.
The parcel scheduling sub-module forms round-tours to deliver all the parcels for each parcel courier in the study area. From each depot, simple heuristics (such as nearest-neighbor and 2-opt) are used to form efficient tours that respect the vehicle capacity (in terms of number of parcels) and maximum shift lengths for drivers. This, in turn, leads to trip matrices that can be assigned to the network to arrive at network statistics such as vehicle kilometers and emissions.
The conceptual architecture of this module has been applied in four modelling systems:
(1) the strategic freight model of Flanders (SVRM);
(2) the travel demand model of the Municipality of Amsterdam (VMA);
(3) the module for logistical decision-making of the national strategic freight model of the Netherlands (BasGoed);
(4) and the tactical freight simulator of the HARMONY project for the European Commission (test bed: Province of Zuid-Holland, the Netherlands).
In this research we will analyze the impacts of different scenarios and policies, for this purpose we will use the HARMONY implementation of the model in Zuid-Holland. The scenarios explore the impacts of different developments for:
• increased demand for parcels;
• horizontal collaboration between couriers, with shared use of depots;
• a zero-emission zone in Rotterdam, in combination with consolidation centers at the outskirts of the city.
3. Conclusion and future works
A disaggregate region-wide simulation model for parcel deliveries is necessary to evaluate the impacts of policies and developments in e-commerce. Using the developed model, we can show, for example, that vehicle kilometers do not increase linearly with parcel demand due to increased consolidation and that the impacts of zero-emission zones can be diffuse due to rerouting of van trips.
Future efforts may focus on modelling the whole transport chain of e-commerce, rather than only the last-mile deliveries. Furthermore, a demand model for parcels to businesses in line with the model for households is desired, this would require additional data collection.
The emergence of e-commerce in the past decade and the surging growth during the pandemic, partially at the cost of in-store shopping, have reinforced the need for a better representation of this type of consumer demand and its effects in urban transportation studies (Reiffer et al, 2021). Since this is a recent development, conventional passenger transport models only model the personal mobility for in-store shopping. Standard modelling tools for large-scale demand forecasts for online and in-store shopping are limited. A proper representation of this demand segment first of all requires an estimate of e-commerce demand, and second the simulation of the delivery of the orders. Jaller and Pahwa (2020) developed both an econometric MNL model for in-store and online shopping and applied it to a synthetic population to estimate externalities of the alternatives. The econometric model explains the preferences for type of shopping but not the total level of product consumption, and the delivery of online orders is estimated on aggregate statistics. Other disaggregate simulation studies only focus on e-commerce demand, without considering the trade-off between online versus in-store shopping, such as Cheng et al (2021). In effect, online ordering may reduce physical movements of people to stores, while increasing the delivery of orders to people’s home addresses. This shift is taking place for many consumer products and groceries as well. Weltevreden and Rotem-Minaldi (2007) show early evidence that e-commerce ordering in the Netherlands increases freight transport, while personal travel decreases marginally. On the side of e-commerce deliveries, the simulation of urban freight transport is a well-studied topic in recent literature (Mommens et al, 2021; Hörl and Puchinger, 2021; Reiffer et al, 2021). However, modelling the demand side of e-commerce is often still minimal.
2. Methodology, results and main contributions
We present an empirical e-commerce demand model that is implemented in an urban freight simulator developed in the H2020 project HARMONY (Kamargianni et al, 2020). This new demand model for e-commerce is now a part of the simulator’s parcel module, which generates delivery tours based on the parcel demand by households and businesses. We estimated an ordered logit model with the demand for e-commerce shipments to households as the dependent variable, based on the assumption that one online order equals one parcel, as a function of personal and household characteristics which are known within the simulator.
A second model, connecting e-commerce with the demand for traditional in-store shopping, is also presented here, albeit not yet implemented in the urban freight simulator. In this model we first estimated total consumer demand separately for groceries and non-groceries, and next an adoption model for e-commerce services. The model has the structure of a two-step logit model: an ordered logit model for the total consumer demand, and next a binary choice model for the choice between online and in-store shopping for each of the shopping occurrences that make up a person’s consumer demand.
The models are estimated on the Mobility Panel Netherlands, the MPN (Hoogendoorn-Lanser et al, 2015). The 2017 wave of the MPN contained additional questions regarding online and in-store shopping that can be used for the estimation of choice models. To make the models suitable for application in the freight simulator, we focused on explanatory variables that differ between locations (i.e., zones in a model). The most important variables in the choice models that explain the spatial pattern of e-commerce demand are household income, age of the respondent (in 10 categories) and urbanization level at household location. Other personal characteristics that do not vary spatially are included if they improve the explanatory power of the models (e.g., gender).
3. Conclusion and future works
Age and household income are important predictors for the adoption of e-commerce and the number of parcels ordered. The age-classes 18-39 have the highest preference for e-commerce ordering. Above 40, the preference for e-commerce steadily declines. Persons living in households in the highest income classes (more than 67,000€ per year) are the most likely adopters of online ordering for both groceries and non-groceries. The urbanization level does not affect the adoption of e-commerce services for non-groceries, but strongly for groceries. This can be explained by the limited availability of e-groceries in less urbanized areas, especially at the time of data collection in 2017.
The presented e-commerce demand model has been implemented in the HARMONY Tactical Freight Simulator where it is used to calculate the number of parcels delivered in an area, the subsequent delivery tours and their effects on traffic and emissions. As the explanatory variables differ between zones, we obtain spatially distinct effects. In a next step, the presented model can be linked to a passenger simulator to jointly model and assess the generation of shopping trips and parcel deliveries. Another important research topic is the formulation of representative growth scenarios for e-commerce demand. As online ordering adoption rates evolve over the coming decade, socio-economic developments alone will likely not be sufficient to explain them. Adequately representing the evolution of these adoption rates in transport models requires a tailored calibration approach.
...
The emergence of e-commerce in the past decade and the surging growth during the pandemic, partially at the cost of in-store shopping, have reinforced the need for a better representation of this type of consumer demand and its effects in urban transportation studies (Reiffer et al, 2021). Since this is a recent development, conventional passenger transport models only model the personal mobility for in-store shopping. Standard modelling tools for large-scale demand forecasts for online and in-store shopping are limited. A proper representation of this demand segment first of all requires an estimate of e-commerce demand, and second the simulation of the delivery of the orders. Jaller and Pahwa (2020) developed both an econometric MNL model for in-store and online shopping and applied it to a synthetic population to estimate externalities of the alternatives. The econometric model explains the preferences for type of shopping but not the total level of product consumption, and the delivery of online orders is estimated on aggregate statistics. Other disaggregate simulation studies only focus on e-commerce demand, without considering the trade-off between online versus in-store shopping, such as Cheng et al (2021). In effect, online ordering may reduce physical movements of people to stores, while increasing the delivery of orders to people’s home addresses. This shift is taking place for many consumer products and groceries as well. Weltevreden and Rotem-Minaldi (2007) show early evidence that e-commerce ordering in the Netherlands increases freight transport, while personal travel decreases marginally. On the side of e-commerce deliveries, the simulation of urban freight transport is a well-studied topic in recent literature (Mommens et al, 2021; Hörl and Puchinger, 2021; Reiffer et al, 2021). However, modelling the demand side of e-commerce is often still minimal.
2. Methodology, results and main contributions
We present an empirical e-commerce demand model that is implemented in an urban freight simulator developed in the H2020 project HARMONY (Kamargianni et al, 2020). This new demand model for e-commerce is now a part of the simulator’s parcel module, which generates delivery tours based on the parcel demand by households and businesses. We estimated an ordered logit model with the demand for e-commerce shipments to households as the dependent variable, based on the assumption that one online order equals one parcel, as a function of personal and household characteristics which are known within the simulator.
A second model, connecting e-commerce with the demand for traditional in-store shopping, is also presented here, albeit not yet implemented in the urban freight simulator. In this model we first estimated total consumer demand separately for groceries and non-groceries, and next an adoption model for e-commerce services. The model has the structure of a two-step logit model: an ordered logit model for the total consumer demand, and next a binary choice model for the choice between online and in-store shopping for each of the shopping occurrences that make up a person’s consumer demand.
The models are estimated on the Mobility Panel Netherlands, the MPN (Hoogendoorn-Lanser et al, 2015). The 2017 wave of the MPN contained additional questions regarding online and in-store shopping that can be used for the estimation of choice models. To make the models suitable for application in the freight simulator, we focused on explanatory variables that differ between locations (i.e., zones in a model). The most important variables in the choice models that explain the spatial pattern of e-commerce demand are household income, age of the respondent (in 10 categories) and urbanization level at household location. Other personal characteristics that do not vary spatially are included if they improve the explanatory power of the models (e.g., gender).
3. Conclusion and future works
Age and household income are important predictors for the adoption of e-commerce and the number of parcels ordered. The age-classes 18-39 have the highest preference for e-commerce ordering. Above 40, the preference for e-commerce steadily declines. Persons living in households in the highest income classes (more than 67,000€ per year) are the most likely adopters of online ordering for both groceries and non-groceries. The urbanization level does not affect the adoption of e-commerce services for non-groceries, but strongly for groceries. This can be explained by the limited availability of e-groceries in less urbanized areas, especially at the time of data collection in 2017.
The presented e-commerce demand model has been implemented in the HARMONY Tactical Freight Simulator where it is used to calculate the number of parcels delivered in an area, the subsequent delivery tours and their effects on traffic and emissions. As the explanatory variables differ between zones, we obtain spatially distinct effects. In a next step, the presented model can be linked to a passenger simulator to jointly model and assess the generation of shopping trips and parcel deliveries. Another important research topic is the formulation of representative growth scenarios for e-commerce demand. As online ordering adoption rates evolve over the coming decade, socio-economic developments alone will likely not be sufficient to explain them. Adequately representing the evolution of these adoption rates in transport models requires a tailored calibration approach.
Reducing emissions caused by urban freight transportation is an increasingly important policy objective for transportation planners around the world. New and innovative ways of data collection provide new possibilities to analyze these issues. In this paper we present MASS-GT, a new multi-agent simulation system for urban goods transport. The empirical basis is provided by an exceptionally large dataset of truck trip travel diaries for The Netherlands that was collected from transportation management systems using an automated data collection interface. The dataset is very dense and includes information on vehicles, routes, and shipments carried. The strategic part of the model simulates the formation of individual shipments based on logistic processes at a strategic level, such as sourcing, distribution channel choice and shipment size choice. At tactical level disaggregate choices are simulated for tour formation, vehicle type- and time of day choice, based on observed distributions. The multi-agent approach allows to implement heterogeneous preferences and thus differentiated responses to new policies. We present an application of the model to study the impacts of urban consolidation centers (UCC) and zero emission zones. The freight transportation volumes transported to these UCC and their impact on logistic indicators are analyzed. Simulation results show that vehicle kilometers travelled within the wider region increase with the introduction of UCC, and at the same time the efficiency of deliveries increases as well. Thus the model allows to study trade-offs between regional and local systems that emerge from different behavioural responses to policies.
Om de impact te toetsen passen we een gedesaggergeerd simulatie model toe om te verkennen welke emissiereductie haalbaar zijn als maatregelen worden gehanteerd. De decarbonisatie scenario's zijn gebaseerd op een verkenning van de literatuur naar mogelijke maatregelen om de emissies van het wegvervoer te reduceren.
...
Om de impact te toetsen passen we een gedesaggergeerd simulatie model toe om te verkennen welke emissiereductie haalbaar zijn als maatregelen worden gehanteerd. De decarbonisatie scenario's zijn gebaseerd op een verkenning van de literatuur naar mogelijke maatregelen om de emissies van het wegvervoer te reduceren.
Results shows that the impact of UCCs is not trivial: we can see a small increase in vehicle kilometers travelled (VKT) overall: +0.25% which can be attributed to the rerouting of shipments through the UCCs. Calculations confirm that emissions are reduced dramatically, by 90%, inside the ZEZ. At the city scale this corresponds to a reduction of almost 10%, as most freight related traffic is generated by the port and involves long haul HGV transport that do not enter the city center. At regional level the reduction of impacts is very small. More measures are needed of more ambitious reductions in emissions are to be achieved.
...
Results shows that the impact of UCCs is not trivial: we can see a small increase in vehicle kilometers travelled (VKT) overall: +0.25% which can be attributed to the rerouting of shipments through the UCCs. Calculations confirm that emissions are reduced dramatically, by 90%, inside the ZEZ. At the city scale this corresponds to a reduction of almost 10%, as most freight related traffic is generated by the port and involves long haul HGV transport that do not enter the city center. At regional level the reduction of impacts is very small. More measures are needed of more ambitious reductions in emissions are to be achieved.
It was twenty years ago today
Revisiting time-of-day choice in the Netherlands
Time-of-day (TOD) choice can be considered as a fifth stage in the modelling of transport behaviour, additional to the conventional four stages. Twenty years ago in The Netherlands, a stated preference (SP) study was designed for investigating the choice of time-of-day (departure time) and transport mode. A nested logit time period and mode choice model, largely based on this SP data set, was included as one of the components of The Netherlands national transport model (LMS). A new TOD SP survey has now been developed to obtain up-to-date information for the next re-estimation round of the LMS. The fieldwork was carried out in in 2019, followed by the re-estimation of the nested logit model of period and mode choice on the new SP data. The context for the SP is that of a tour (round trip) carried out by the respondent as car driver or by train, also distinguishing by travel purpose (commuting, business, education and other). This means that we are asking questions both about the outward leg of the tour and the inward leg. Both car drivers and train users are asked to participate in two SP experiments on TOD and mode choice: the first focussing on the trade-off between congestion or crowding and the departure/arrival times; the second also with differentiation in costs between peak and off-peak. Our tentative conclusion is that TOD choice seems to have become (relatively to mode choice) more flexible in the past two decades, in line with the trends towards more flexibility in scheduling activities over the day and a 24 hours economy. Moreover, we now estimate nest coefficients for both car drivers and train users (until now the assumption that had to be made in the LMS was that the nest coefficients for train followed those for car).
goederenvervoer gemodelleerd. In deze paper beschrijven we een tourformatie algoritme voor goederenwegvervoer binnen Nederland. Dit algoritme wijst zendingen toe aan ritten. Twee probabilistische keuzestappen in dit algoritme zijn geschat op empirische data over Nederlandse beroepsvervoerders. Relevante strategieën en randvoorwaarden in tourformatie komen terug in dit algoritme. Daarnaast worden verschillen in tourformatie voor diverse locaties, goederensoorten, en voertuigtypen overwogen. Zo worden op goederenoverslagplaatsen meestal ritten met een enkele zending gevormd, terwijl vanaf distributiecentra vaker ritten met meerdere zendingen voorkomen.
Toepassing van het algoritme laat zien dat geobserveerde ritpatronen uitstekend gereproduceerd worden. ...
goederenvervoer gemodelleerd. In deze paper beschrijven we een tourformatie algoritme voor goederenwegvervoer binnen Nederland. Dit algoritme wijst zendingen toe aan ritten. Twee probabilistische keuzestappen in dit algoritme zijn geschat op empirische data over Nederlandse beroepsvervoerders. Relevante strategieën en randvoorwaarden in tourformatie komen terug in dit algoritme. Daarnaast worden verschillen in tourformatie voor diverse locaties, goederensoorten, en voertuigtypen overwogen. Zo worden op goederenoverslagplaatsen meestal ritten met een enkele zending gevormd, terwijl vanaf distributiecentra vaker ritten met meerdere zendingen voorkomen.
Toepassing van het algoritme laat zien dat geobserveerde ritpatronen uitstekend gereproduceerd worden.