In practice, revenue management studies are often driven by the price elasticity of the demand, which models the relation between the change in demand and the price of travel. In an activity based simulation model these elasticities are an emergent property of the scoring function that uses the activities and travelling to compute
4.8 Conclusions 97
utilities. As a result, activity based models depend on parameters for every activity and parameters for travelling. Based on all these parameters, the price elasticity of the demand becomes an emergent property rather than something which is put directly in the model. One potential advtange of this approach is that in some situations it can be easier to model activities of the population that to perform economic experiments to obtain the price elasticity. Another potential advantage is that it may be easier to include non-linear interactions between the demand and congestion, other modes of transport or properties of the activities than to model the price elasticity for each and every situation that may occur.
There are also disadvantages to this approach. First, the behavior of the population is quite sensitive to the parameters of the activity based model. In one preliminary study we increase the typical duration of the home activity to twenty hours, which resulted in a time shift of the afternoon peak. This raises the important question how the different parameters of the activity based model influence the price elasticity of the demand, and to which extent this relation is realistic for practical applications.
An interesting area for future research is to explore in detail how the price elasticity is influenced by the different parameters of the model and the price elasticity of the demand. To gain better understanding of the emergence of a macroscopic such as the price elasticity, it makes sense to use simple scenarios with a single public transport line with a few stops and investigate the trade offs a single agent can make with regard to travel times and the resulting utilities. While there might be potential for analytical results in this area, it is also possible to simulate a number of scenarios with different parameters and compare the plans and corresponding utilities emperically.
4.8 Conclusions
We have shown how we can use smart card data to generate different types of demand. We developed an agent-based simulation that allows us to analyze the movements of the agents through our multimodal public transport network. We experimented with different settings for the number of trip-based agents and with three discount policies in the off-peak hours. Finally, we discussed several opportunities for future research.
As soon as we sorted our dataset in such a way that we could process all journeys customer by customer in chronological order, demand generation could be done very efficiently. We used simple rules to determine whether a customer should be modeled using trip based, tour based or pattern based demand. We have evaluated the impact of different thresholds for the pattern based customers on the resulting approximate equilibrium. We have also seen that an off-peak discount can be used to let a part of the agent population shift their travel times. In our case, this lead to a lower revenue. However, the effect on the required capacity must be taken into account when making a trade off between costs and revenue.
There are many opportunities for future research. First, our simulation can greatly benefit from proper calibration. Configuring the behavior and economic parameters
96 Recognizing Demand Patterns
4.7.4 Validation
Validating a simulation like this is not a trivial task. One aspect that we can validate is the question whether the simulation can be used as a predictive tool for the movement of passengers through a public transport network. The straightforward way to do this is by splitting the dataset at a certain moment in time. We then use the first part of the dataset to generate agent populations and compare the outcomes to what is observed in the second part of the dataset. At first, we should choose a moment within a period where no policy and scheduling changes have occurred. If we can pass this test, we can raise the bar by choosing the moments at which a policy change has occurred, such as the introduction of a new schedule or new pricing schemes.
Another aspect that we may want to validate, is the question whether the emerging activity patterns of the agents represent the real-life activity patterns of the passengers represented by the agents. Validating this aspect requires much greater effort than validating the movements of passengers. One approach could be to use survey data containing activity logs registered in diaries and compare the diaries to the activity plans in the simulation. There may be some privacy issues with this approach, since it would require that we link the smart card id’s to the participants, in order to match a diary to an agent. A possible workaround is to generate dummy check-in/check- out data from the diaries by generating a check-in and a check-out for the journeys documented in the diaries. We could then use this dataset as if it were a smart card dataset and investigate to what extent the generated activity patterns of the agents reproduce the original activity plans.
In a similar way, we can consider the study of other location tracking datasets, such as triangulation logs from mobile phone operators or the location logs from the mobile phones themselves. The main advantage is that such a dataset contains more details on the whereabouts of individuals, which gives more opportunity to estimate what they are doing. When using smart card data, we may observe that a person checks out at a station near a shopping mall and checks in four hours later. However, we have no data to decide whether it is probable that this person has been shopping or that this person has been working as an employee at one of the stores. If we have a mobile phone log, we may observe that the person has visited a great number of stores during these four hours. This would be evidence that he was not working as an employee. However, mobile phone data often comes as a set of observations in time and space without device id’s to protect the privacy of their owners. As a result, this direction of research has severe challenges.
4.7.5 Economic Modelling
In practice, revenue management studies are often driven by the price elasticity of the demand, which models the relation between the change in demand and the price of travel. In an activity based simulation model these elasticities are an emergent property of the scoring function that uses the activities and travelling to compute
4.8 Conclusions 97
utilities. As a result, activity based models depend on parameters for every activity and parameters for travelling. Based on all these parameters, the price elasticity of the demand becomes an emergent property rather than something which is put directly in the model. One potential advtange of this approach is that in some situations it can be easier to model activities of the population that to perform economic experiments to obtain the price elasticity. Another potential advantage is that it may be easier to include non-linear interactions between the demand and congestion, other modes of transport or properties of the activities than to model the price elasticity for each and every situation that may occur.
There are also disadvantages to this approach. First, the behavior of the population is quite sensitive to the parameters of the activity based model. In one preliminary study we increase the typical duration of the home activity to twenty hours, which resulted in a time shift of the afternoon peak. This raises the important question how the different parameters of the activity based model influence the price elasticity of the demand, and to which extent this relation is realistic for practical applications.
An interesting area for future research is to explore in detail how the price elasticity is influenced by the different parameters of the model and the price elasticity of the demand. To gain better understanding of the emergence of a macroscopic such as the price elasticity, it makes sense to use simple scenarios with a single public transport line with a few stops and investigate the trade offs a single agent can make with regard to travel times and the resulting utilities. While there might be potential for analytical results in this area, it is also possible to simulate a number of scenarios with different parameters and compare the plans and corresponding utilities emperically.
4.8 Conclusions
We have shown how we can use smart card data to generate different types of demand. We developed an agent-based simulation that allows us to analyze the movements of the agents through our multimodal public transport network. We experimented with different settings for the number of trip-based agents and with three discount policies in the off-peak hours. Finally, we discussed several opportunities for future research.
As soon as we sorted our dataset in such a way that we could process all journeys customer by customer in chronological order, demand generation could be done very efficiently. We used simple rules to determine whether a customer should be modeled using trip based, tour based or pattern based demand. We have evaluated the impact of different thresholds for the pattern based customers on the resulting approximate equilibrium. We have also seen that an off-peak discount can be used to let a part of the agent population shift their travel times. In our case, this lead to a lower revenue. However, the effect on the required capacity must be taken into account when making a trade off between costs and revenue.
There are many opportunities for future research. First, our simulation can greatly benefit from proper calibration. Configuring the behavior and economic parameters
98 Recognizing Demand Patterns
of the agents is far from trivial. This requires surveys with representative groups and expert knowledge that the various public transport companies might or might not be willing to share. The amount of work this requires will likely result in a model that is better suited for practical applications, but it does not necessarily answer how we can use methods and ideas from complexity science in the modeling and optimization of public transport processes. Including heterogeneity in the price sensitivity of is also an important step to include more accurate economic behavior of the agents, although this would be mostly a software development challenge that should not be too difficult to overcome, given the open structure of the MATSim framework.
Other opportunities for future research are the method for demand generation, which can be improved upon. One way is to take a closer look at the smart card data itself and apply data mining techniques to create a number of different behavioral types. Another opportunity is to combine the smart card data with additional datasets. We believe this line of research to be better suited within the framework of complexity
science, as it is concerned with the relationship between emerging patterns and
microscopic travel behavior.
We believe that an improved version of our simulation, where economic behavior is realistic and properly validated, can be helpful in both the design of revenue management systems, including location based and modality based tariff schemes and other fields of study within a public transport context.
5
Detecting Activity Patterns from Smart Card Data
Co-authors : Evelien van der Hurk, Leo Kroon, Ting Li and Peter Vervest This paper has been accepted after peer review for presentation at, and publication in the proceedings of, the 25th Benelux Conference on Artificial Intelligence (BNAIC2012).
5.1 Introduction
One of the most valuable pieces of information during the development and opera- tional planning of passenger transportation systems is passenger demand. Under- standing how demand develops allows governments and public transport operators to assess the profitability of infrastructure investments. By changing the infrastruc- ture or by developing a new area, new travel routes and new purposes to travel are created. Transportation is a major concern in the cost-benefit analysis of such large projects. Passenger demand also serves as input for the design of public transport systems, where they are important when decisions on which lines to operate, the service frequencies and the assignment of vehicles of different capacities to each service are made.
Traditional demand models typically estimate an origin-destination matrix of trips and use a traffic assignment model to map routes to OD-pairs in the transportation network. Typical data sources for such matrices are counts of observed travellers. Especially in public transport counts can be used to obtain an origin-destination matrix within a reasonable processing effort. However, counts cannot be obtained for cases where we consider a change in infrastructure or service. To overcome this problem other data sources, such as data related to land use and travel-choice surveys are used to make predictions of demand in other scenarios. One of the drawbacks of
98 Recognizing Demand Patterns
of the agents is far from trivial. This requires surveys with representative groups and expert knowledge that the various public transport companies might or might not be willing to share. The amount of work this requires will likely result in a model that is better suited for practical applications, but it does not necessarily answer how we can use methods and ideas from complexity science in the modeling and optimization of public transport processes. Including heterogeneity in the price sensitivity of is also an important step to include more accurate economic behavior of the agents, although this would be mostly a software development challenge that should not be too difficult to overcome, given the open structure of the MATSim framework.
Other opportunities for future research are the method for demand generation, which can be improved upon. One way is to take a closer look at the smart card data itself and apply data mining techniques to create a number of different behavioral types. Another opportunity is to combine the smart card data with additional datasets. We believe this line of research to be better suited within the framework of complexity
science, as it is concerned with the relationship between emerging patterns and
microscopic travel behavior.
We believe that an improved version of our simulation, where economic behavior is realistic and properly validated, can be helpful in both the design of revenue management systems, including location based and modality based tariff schemes and other fields of study within a public transport context.
5
Detecting Activity Patterns from Smart Card Data
Co-authors : Evelien van der Hurk, Leo Kroon, Ting Li and Peter Vervest This paper has been accepted after peer review for presentation at, and publication in the proceedings of, the 25th Benelux Conference on Artificial Intelligence (BNAIC2012).
5.1 Introduction
One of the most valuable pieces of information during the development and opera- tional planning of passenger transportation systems is passenger demand. Under- standing how demand develops allows governments and public transport operators to assess the profitability of infrastructure investments. By changing the infrastruc- ture or by developing a new area, new travel routes and new purposes to travel are created. Transportation is a major concern in the cost-benefit analysis of such large projects. Passenger demand also serves as input for the design of public transport systems, where they are important when decisions on which lines to operate, the service frequencies and the assignment of vehicles of different capacities to each service are made.
Traditional demand models typically estimate an origin-destination matrix of trips and use a traffic assignment model to map routes to OD-pairs in the transportation network. Typical data sources for such matrices are counts of observed travellers. Especially in public transport counts can be used to obtain an origin-destination matrix within a reasonable processing effort. However, counts cannot be obtained for cases where we consider a change in infrastructure or service. To overcome this problem other data sources, such as data related to land use and travel-choice surveys are used to make predictions of demand in other scenarios. One of the drawbacks of
100 Detecting Activity Patterns from Smart Card Data
using origin-destination matrices is that it is not straightforward to make a matrix time-dependent, analyze the behavior of specific groups of travellers (outside of an origin-destination gorup) or to model more detailed interactions between passengers and public transport, such as for example due to crowding.
Activity-based models (Axhausen and Gärling, 1992) provide an improvement in this regard. The main idea of this paradigm is that transport demand emerges from many individual desires to perform certain activities at different locations at certain times. Instead of defining a large matrix as demand, a list of activity plans is created. In a simulation agents can perform these activities in a simulated transportation network. It is possible to observe how the agents decide to travel from activity to activity within the simulation. An example implementation of such a model is the open source agent-based transport simulation package MATSim (Horni et al., 2016), that has been applied at different locations around the world. The input required for such models consists of individual day plans that define a chain of activities. Since this input data cannot be directly deducted from an OD-matrix, random plans generated from economic and geographical data are often combined with travel diaries collected through surveys.
In this chapter we develop a method to deduce and analyse activity patterns and activity sequence patterns within the time dimension. We define an activity as a combination of a time interval and a location. These activities are reconstructed from the set of trips stored in the data for a specific person. Using both clustering and labeling methods, we identify important activity time intervals and analyse common activity chains. We consider a time interval to be important if it represents at least 10% of the activities at a station in the network. We are not only able to identify home-work patterns, but also identify shorter activities. Moreover, the activity chains provide information on the order of different activities. We aim to extend our method to include spatial dimensions in the future, by labeling stations into groups based on the temporal patterns outputted. We believe that the results obtained using our method can provide public transport operators insight into how their network is being used and give valuable input for activity based models.
5.1.1 Related Work
Pelletier et al. (2011) present an excellent general review of smart card data research in