Many of the aforementioned contributions of this thesis can be attributed to the simplicity of the methodology proposed here. However, accessibility and segregation are complex phenomena that involve many factors that were not included in the AxS model, or were included in an oversimplified manner. This section discusses the limitations of this study, resulting from the model’s design and implementation simplifications.
Representation of space and agents’ movement
The choice of environmental representation was made with simplicity and scal- ability in mind. However, a raster environment is inherently more limited than a network-based environment for simulating movement. The main shortcoming of raster environments is the lack of an accurate representation of the street- network’s topological structure. This means factors such as connectivity, turn restrictions, and directionality of streets are absent in the model’s environment. Those limitations negatively affect the model’s results both at larger and smaller scales, as discussed in the following paragraphs.
The lack of a topological structure makes it more difficult to identify routes where the most efficient trajectory contains large deviations from a rela- tively direct path. Such deviations can be found in many circumstances in the real world. For example, a motorway may circle around a densely urbanised area to avoid heavy urban traffic. In those situations, the longer motorway route can be more efficient in terms of travel time than the most direct route through heavily congested areas. The model’s pathfinding algorithm is notably less accurate on those situations, as demonstrated by the validation exercises reported in chapter 6.
The aforementioned route deviations can also be due to the existence of natural obstacles (or spatial constraints) impossible to traverse, such as bodies of water, mountains, or forests. Finding a path around obstacles is relatively simple in network environments, but a significantly harder task in a raster environment. Consequently, the implementation of spatial constraints in the model is subop- timal. Movement, in the model, is just facilitated by the road network, but not constrained to it. Hence, agents need to actively look for those spatial constraints and find a way around them. In some situations, agents can get blocked behind those obstacles and be prevented from reaching their destinations.
The model’s relatively coarse spatial resolution (200 m, in the simulation exercises reported in this thesis) does not allow for capturing fine grained details in the simulated trajectories. At that resolution, only the main road network can be represented. Consequently, the local road network is assumed to be embedded in regular urban cells, where agents can freely walk through. In reality, those local road networks may have many features that restrict movement within them, such as cul-de-sacs and dead ends, that cannot be represented in the model.
Representation of local environmental knowledge
As previously discussed, in reality, individuals make decisions based on their par- tial spatial knowledge. Specifically regarding wayfinding, spatial elements such as Kevin Lynch’s (1960) landmarks and boundaries, as well as the road network’s hierarchic structure, are important in individuals’ decision-making process. How- ever, those elements are absent from the model. Partial knowledge of the environ- ment was indirectly simulated through agents’ field of view, determined by angle of vision and search radius parameters. This is a very simplified representation of the complex cognition processes that take place in people’s brains.
Furthermore, spatial knowledge varies in an individual basis. It is ex- pected to be more comprehensive near places each individual is more familiar with, such as residential and workplace locations, as well as other places the in- dividual spends time in. Conversely, spatial knowledge is non-existent or very limited in places the individual has never been to, or has not visited in a long time. Thus, spatial knowledge is thought to be gradually built over time as the individual interacts with their environment. All those complex dynamics are absent from the model. Even though differences in spatial knowledge between individuals could be represented, in the model, by different values of angle of vision and search radius, that possibility was not explored in this study.
Traffic dynamics
The AxS model simulates trajectories and city wide movement patterns from in- dividual behaviour, but it is not a traffic model. Complex on-road dynamics, such as interactions between drivers, lane-changing behaviour, and traffic congestion, are not considered. Those features are absent from the model because they are out of the scope of this thesis, and it would be very complex and time consuming to build.
Traffic congestion, however, is a recurrent problem in large cities and can potentially affect the accessibility and segregation metrics calculated by the model. Travel speeds and travel times are strongly dependent on congestion levels, which vary between hours of the day and areas of the city. Those travel times are used, in the model, to calculate individuals’ time budgets and accessibility metrics. Consequently, the resulting accessibility metrics may be overestimated when traffic congestion is not taken into consideration. Similarly, the model’s segregation metrics are calculated on individuals’ trajectories, which shape can be affected by congestion levels as individuals find alternative routes in order to avoid congested areas. Hence, the lack of a representation of congested areas is a limitation of this study.
Public transportation system
The AxS model does not include an explicit implementation of the public trans- portation system. Transport modes are represented by different movement speeds as a simple proxy. Agents using public transport move at a uniform speed dur- ing their entire trajectories, following the same pathfinding rules as pedestrians and drivers, which is not ideal. This implementation does not account for the complexity and importance of the urban transportation system, however. Impor- tant elements such as departure/arrival timetables, fixed routes, and designated embarking/disembarking points are absent from the model’s implementation.
The simplified representation of the public transportation system has an impact on the accuracy of the model’s results. Simulated trajectories do not follow actual routes of any specific transport mode. Simulated travel times are also affected, as time wasted while waiting for a bus or train, and during connec- tions between different lines and modes, is not accounted for. Consequently, the validation tests reported in chapter 6 demonstrated that simulated trajectories and travel times of trips made by public transport modes are less accurate than those of trips made by car, which are not affected by those simplifications.
Activity system and temporal patterns
The current implementation of individuals’ activity schedules in the model is very simple, supporting a single activity per agent, which generates a single trip. In the empirical applications of chapter 7, that trip was the commute to work. Implementing full travel and activity diaries as input and adding functionality to simulate trips with multiple stops would be relatively straightforward. However, those improvements depend on data availability, which is very restricted at that level of detail. As a matter of fact, the lack of data was one of the motivations of this study.
Temporal patterns, in the model, are also very simplified in comparison to those patterns in the real world. For example, cities present morning and afternoon peak hours, and individuals work during different hours of the day or night. In the model, all trips are simulated as if happening during the same time window, which can be associated to the morning peak hour. All individuals are also assumed to work during the same hours, which is not true in real life. Hence, the simulations presented in this thesis are to be interpreted as indicators of urban dynamics, and not as accurate reproductions of real-time traffic patterns.
Accessibility measures
The accessibility metrics proposed in this thesis are calculated on individuals’ potential path areas (PPA) generated by the model. Those PPAs, however, are very simplified representations of the areas individuals can actually access. They are simple circular areas around people’s places of residence and work, delimited based on individuals’ movement speeds and time budget. Those circular areas assume uniform travel velocities in all directions, and no restrictions to movement imposed by the road network or transportation system, which are unrealistic assumptions. Also, opportunities located in the trajectories between people’s places of residence and work, that could potentially be accessed in a short stop during commute, are not counted as accessible. Those simplifications were made, mainly, for scalability reasons as PPAs are calculated for each individual agent in the simulation.
Other limitations of the accessibility results presented in this study are related to the availability of data on activities and opportunities accessible to people. Data on opening hours of activities and activity durations (the mini- mum time required to participate on an activity) are seldom available in large scales. Data on quality and variety of activities and on individuals’ needs are also very difficult to obtain. Individual-based accessibility metrics could, potentially, sort available opportunities based on characteristics such as affordability and desirability, for example. This would allow analyses based on the variety of op- portunities inside the individual’s PPA. Additional questions could be answered, such as: Does the mix of opportunities inside the individual’s PPA satisfies all
of the individual’s needs? Is there an overabundance of one type of activity in detriment of another (for example, many fast food restaurants, and few healthier alternatives)? Evidently, those kinds of questions were not explored in this study.
Solving those data limitations is out of the reach of this thesis, as this would require extremely detailed and costly data collection. A simpler assumption was made in this study: the more activity locations are accessible, the more likely an individual is of finding a place that satisfies their needs at different situations. This assumption, however, does not solve the problem completely but leaves open opportunities for future experimentation as more detailed land-use data becomes available.
Segregation measures
The segregation measures proposed in this thesis are calculated based on the arti- ficial trajectories simulated by the model. Hence, the main shortcoming of those metrics is related to the accuracy of the simulated trajectories. As established by the validation tests reported in chapter 6, the accuracy of the trajectories can vary significantly due to factors such as length, transport mode, presence of obstacles along the way, and existence of large detours in the route. As such, the segregation metrics results need to be analysed in light of the limitations of the model’s pathfinding algorithm.
The proposed copresence metric is based on the number of encounters between individual agents in the model’s environment during simulation. This means copresence is a measure of interaction in both space and time, in line with the time geographic framework. However, copresence measures actual encounters in the model, that represent only potential encounters in reality. Directly measur- ing actual encounters of people in reality is a significantly more challenging task and is beyond the scope of this thesis. The model’s low spatial resolution also represents a significant simplification to the idea of an encounter between agents: in the study of London (section 7.2), an encounter means two agents shared a 200m x 200m cell at a given time. This is obviously a simplification and by no means indicate a meaningful interaction has occurred. In that sense, copresence indicates those individuals were potentially exposed to each other at a given time, so the minimum conditions for interaction were met.
The absence of the public transportation representation, in the model, also affects the segregation metrics outputs. When any two agents encounter each other during the simulation, that encounter counts towards the copresence results. However, that encounter is actually not possible if those two agents are using different modes of transport. Furthermore, probabilities of interaction vary widely between transport modes. While pedestrians can interact with other pedestrians, and bus/train riders may interact with other bus/train riders, car drivers are very unlikely to interact with each other in any socially meaningful
way.
The simple temporal patterns simulated by the model also negatively affects the segregation results. As previously discussed, in the simulations con- ducted in this thesis, all individuals were assumed to work and commute during the same hours of the day. However, as that activity pattern is very simplified, it is possible for people who travel through the same areas at entirely different times, thus never encountering each other in real life, to be simulated as if they shared the same space at the same time. Those situations can cause an overesti- mation of interaction potential. However, this is more a limitation of the available datasources than of the model’s design.