# An Intelligent Assistant for Public Transport Management

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5 On the other hand, the goal of the prediction task is to estimate the future impact of the problem. This prediction, estimates the total number of passengers affected by the delay together with the average delay for those passengers. This is calculated for the next T minutes (for instance, T=15 minutes). In particular, for example, this method may compute the values for the impact I and total number of affected persons P: n I = P = i= 1 p i e i n p i i= 1 where p i is the estimated number of persons waiting at the stop i, and e i is the increase of waiting time due to the delay of the bus. The sum is done for the n stops affected in the T minutes. The value I gives a quantitative measure to compare the severity of different problems. Thus, it provides a global criterion to solve conflicts when different problems in different lines need the same resource to improve the service (e.g., a reserve vehicle). The value E = I/P expresses the average increase of waiting time. The prediction task is divided into three subtasks: estimate future demand, simulate behavior and evaluate impact. The first subtask estimates the future demand by using a local knowledge base with historical demand. This includes the expected number of passengers at each stop each interval of time. The second subtask simulates the movement of delayed buses for the next T minutes by using a model of the bus-line (stops, distances, travel-times, etc.) and determines the affected stops. Finally, the third subtask estimates global metrics (e.g. I and E) and uses domain knowledge to interpret the severity of the situations. PLAN: <solve-problem-line-1, solve-problem-line-5, solve-problem-line-10> refined by specialist of the line-1 refined by specialist of the line-5 refined by specialist of the line-10 SUB-PLAN: <vehicle-a33-performs-limitation, reinforce-line-5> refined by specialist in reserve vehicles SUB-PLAN: <vehicle-a15-reinforces-l5-from-s4, informs-vehicle-a33> Fig. 3. Example of development of a plan by the intervention of different specialists that, by turn, refine parts of an abstract plan. The third task, planning, recommends a set of control actions that may solve the detected problems. In this type of reasoning, different classes of specialized heuristic knowledge are typically used to dynamically construct the plan. For example, knowledge about alternative paths of bus lines, criteria about reserve vehicle management, specific criteria to exchange drivers, etc. In order to simulate this type of reasoning it is possible to use a method for hierarchical planning that integrates the idea of skeletal planning [7] and the concept of specialists [8]. The method is based

6 on a search in a hierarchy of goals (specialists) that are knowledgeable about partial abstract plans. Each specialist is responsible of producing a partial plan to solve part of the detected problem. Thus, the knowledge model for this case is represented by a method that divides the planning task into two main subtasks: identify goal and extend plan. The plan is dynamically composed during the reasoning developing a search in such a way that the first subtask identifies the next goal to reach, analyzing the current plan and using a hierarchy of goals (the hierarchy of specialists). Then, the second subtask extends the current plan with partial sub-plans. This process is repeated step by step until the complete final plan is produced. The second subtask is performed by two alternative methods (depending on the level of the hierarchy of goals): (1) one method that selects a plan using heuristic classification knowledge, and (2) another method that constructs the plan by using a domain-specific algorithm with certain parameters (in the bus transport application, there are several instances of this method, for example, the method for reserve vehicles selection). Type of KB Representation Examples Abstraction Rules IF X is-a vehicle, delay of X = D, D > 10 knowledge THEN level-of-delay of X = severe Problem types Historical demand Rules or frames IF X is-a vehicle, full-vehicle-alarm of X = on, current-stop of X = S THEN new problem P, type of P = overflow, affected-vehicle of P = X, location of P = S Table Stop Time-Interval Type-of-day Hist-Demand S1, 6:30-9:30, weekday, 20, S2, 6:30-10:00, weekday, 15,... Network model Objects Object L3 is a Line. Attributes: Stops: (S1,S7,S15,S18,S21,S45,S56,S78),..., Impact categories Rules IF X is-a line, affected-passengers of X > 50, average-delay of X > 5 THEN impact-level of X = severe Hierarchy of goals Skeletal plans library Plan composition Domain specific parameters Table Specialist Goals Pre-cond. Post-cond Reinforcement, G1, G2,... A1,... A2,... Limitation, H1, H2,... A3,... A4,... Rules IF X is-a problem, type of X single-severe-delay, affected-vehicle of X = V THEN new plan P, type of P = reinforced-limitation, affected-vehicle of P = V Frames PLAN reinforced-limitation. ACTION TYPE concrete: vehicle X performs limitation ACTION TYPE abstract: vehicle X is reinforced... Table Parameter Value max-number-of-stops-to-regulate, 5, min-percentage-of-absorption, 80%,... Fig. 4. Symbolic representation of knowledge bases for the model of public transport management.

7 Figure 3 shows an example about how a plan is developed by the intervention of several specialists. In the example, first, a global plan is produced with three abstract actions that respectively are associated to line-1, line-5 and line-10. The corresponding specialist refines each abstract action. For example, the second action is refined by the specialist of the line-5 and produces a sub-plan with two actions. In addition to that, the second action of this sub-plan needs to be refined by another specialist. In summary, according to the previous description, the model includes a total of 9 types of knowledge bases. Figure 4 shows the symbolic representation followed by each type of knowledge base. Thus, for instance, the knowledge base that supports the abstract task can be represented with if-then rules with a forward-chaining inference method. This representation is very flexible and intuitive to formulate a wide range of expressions for qualitative interpretation. The knowledge base for problem types can be represented either with rules or frames. Here, the antecedent of a rule can express the set of conditions for a certain type of problem and the consequent determines the characteristics of the deduced problem. Frames can be also used here, where each frame represents a type of problem with a set of slots that expresses a set of single conditions. The knowledge base for historical demand can be represented with a table that relates each stop of a line with its historical demand according to the type of day and the time interval. For the knowledge base of the network model, an object-oriented representation can be used with concepts such as stop, line, vehicle, etc. and attributes with values that characterize the concepts and establish relations between concepts. The knowledge base for impact categories can be also represented using if-then rules to interpret the quantitative values about the expected delays. Concerning the knowledge related to the planning task, the knowledge base about the hierarchy of goals can be represented with a table that relates each type of goal with the corresponding specialist and the conditions to be considered. The knowledge base for skeletal plans library includes the existing types of plans. Here, rules can relate conditions of the problem with the corresponding type of plan. The knowledge base for plan composition expresses the set of actions in which a plan is decomposed. For this purpose, frames can be used in such a way that each frame identifies a plan and the set of slots identifies the structure of the plan with abstract or concrete actions. Finally, when a specialist is supported by a set of parameters, the knowledge base with domain specific parameters include the corresponding values. It is important to note that these 9 types of bases can produce a higher number of specific knowledge bases in a particular model because some of the bases are specified with different contents for each bus line. Thus, in a concrete model for several bus lines, some knowledge bases can be shared for the management of all the lines but other knowledge bases need to be specified for each particular line. Thus, for instance, a particular model with N bus lines could include: 1 abstraction knowledge base, 1 knowledge base for problem types, N knowledge bases for historical demand, N knowledge bases for network model, 1 knowledge base for impact categories, 1 for hierarchy of goals, 1 for skeletal plan library, 1 for plan composition, M for domain specific parameters (where M is the number of specialists that use domain specific algorithms to dynamically construct a partial plan).

8 4 Applications The model described in this paper was applied for the development of two different applications. The first application was developed within an international project in Europe, called Fluids [9], funded by the European Commission within the Telematics Application Programme. One of the main goals of this project was to provide advanced methods for user-system interaction in the context of real-time decision support. The design methods developed in the project were demonstrated in different applications for transport. In particular, a realization was developed for the case of public transport management for the city of Torino (Italy). The analysis of this problem produced a first version of the general approach presented in this paper. Fig. 5. User interface of the Fluids application for public transport management. Figure 5 shows an example of the user interface developed for the Fluids project (it was tested on-line in the control center in 1998). The user interface combines a dialogue based on a prefixed set of questions for decision support together with a presentation created dynamically that integrates text and an animation to clarify with illustrations how to carry out specific control actions for vehicle management. From the point of view of implementation, the development of this type of systems is a complex task because it requires to integrate different types of problem solvers with different types of knowledge bases. The final architecture must be both efficient for real time operation and flexible to accept changes according to the identification of new strategic knowledge. Thus, in order to help in the final implementation of the

9 system, for the case of the Fluids application, we used a software environment called KSM [11] that has been already used for the development of other applications in the field of transport [12]. This environment is an advanced knowledge engineering tool that provides a model-based approach and a set of software components to facilitate the development. The Fluids application was followed by another application for the city of Vitoria (Spain) which included some improvements compared to the initial version. It was conceived in a general way, according to the model described in this paper, to be reused for different public transport networks. This realization was developed and integrated on real-time in 2001 with the rest of the information system for the management of the bus lines of the control center of the city of Vitoria (Spain). The model includes the management criteria for a total of 15 bus lines. Figure 6 shows the global user interface of this application. Fig. 6. User interface of the application for public transport management for the city of Vitoria. The implementation of this system was performed following a particular advanced software architecture that integrates the set of inference procedures and knowledge bases using a common working memory together with an explicit control mechanism. The user can access to the knowledge model using a particular user interface developed for this purpose in order to provide the required flexibility for knowledge model maintenance.

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