7.3.1. Connections

Using public means of transport is nowadays a major phenomenon at all levels, either at the daily commute to work, for intra-urban transport or other types. For a transit customer, the arrival at destination often requires changing vehicle several times in the connecting platforms, and it is at this stage of the journey that the quality of the transport network is truly perceptible to the user. This notion of connection becomes crucial when we want to modify or optimize a transport network to best meet customer demand or material constraints. However, research has not made a significant contribution in this area in the face of the evolving demands of the transport customer. These customers, who demand a high quality in terms of comfort and safety, have difficulty in complying with the queuing discipline. This difficulty prompted operators to connect lines together by setting up connection points and ensuring faster travel. Connection platforms are hubs for the exchange of passengers. This can be achieved by pooling a stretch of line or stations.

The quality of service perceived by the public transport user depends, on the one hand, on the true-to-life quality of the service delivered, and on the way users interpret such reality on the other hand. The relationship between the real and the perceived is interesting because it characterizes the relationship between the provider and the users. Considering the customer’s demands and temporal aspects, the main elements affecting the customers during their journey connections are:

• – waiting time: for public transport users, the connecting trip is slow enough compared to an ideal trip with zero waiting. The waiting time during a connection is perceived as wasted time and often felt like a problem;
• – travel management: the difficulty of public transport users to manage the itinerary of their trips lies in the state and the manner in which connections are established. Planning is as difficult as real-time travel management:
  • - travel planning is an additional burden for transit users to optimize their route on a complex network. They must define the lines, identify the hubs and estimate travel times;
  • - another charge during the trip requires transit users to constantly identify the connecting platforms and to remain attentive so as not to miss the passage of the bus they must take to reach the connecting hub;
• – psychological state: studies done on transport networks showed the evolution of the psychological state of public transport users during their journeys. These studies concluded that users are subject to minimal stress due to the uncertainty of the evolution of their trip. This stress increases slightly during connection moments. It greatly increases when the users learn that there are disturbances in the network and that they are likely to reach their destination with a delay.

The ideal is to provide transit users with reliable information to manage their movements and avoid delays in case of disturbances.

7.3.2. The regulation of an urban collective transport network

The operation of an urban collective transport network essentially comprises two major parts. After elaborating a production program which is materialized in the preparation of a timetable (TT), the regulation, which is the most difficult part, has to be done: this implies an adaptation of the program to the real conditions which is translated by a real-time schedule controlled by the regulatory strategies adopted by the regulators and the experts of the urban transport network. More precisely, it represents the process of real-time updating of the schedule to the operating conditions. Figure 7.2 shows the characteristics of this regulation.

The task of regulation is complex. It has four parts:

• – knowledge acquisition: this element is key for the notion of regulation. In order to suggest control strategies, the controller relies on criteria such as the status of the buses during connections (delay/ahead of time) or their load. This kind of information represents the basis for regulation and is therefore necessary for it to be well defined. It derives from two different sources:
  • - the Operating Assistance System (OAS);
  • - the direct communication with drivers who can report all the available information to the controller, such as the emergence of incidents;
• – knowledge analysis: after the acquisition of knowledge, the regulator sorts the information and keeps only that which is considered as determining for regulation. This operation is done in a traditional way, based on professional experience and sometimes on pre-defined regulation standards;
• – decision-making: after performing the knowledge analysis, the regulators use their know-how for suggesting regulatory strategies while meeting the requirements of service quality, predefined in collaboration with the network’s managers, as well as the operational constraints. They may therefore require the drivers to perform certain maneuvers or not be involved if they judge that the incident has no impact on the network nor on the satisfaction of collective transport users (service quality);
• – implementation: after deciding and defining the regulatory strategies, the controller transmits them and orders the drivers to apply the various control maneuvers by direct communication through a radio. In return, the drivers inform the regulator about the impact that the decisions made had on the state of the network, as well as the consequences on the buses and possibly on users.

Following these different tasks that the regulator fulfills, we become aware of the complexity of regulation, especially when many incidents occur simultaneously. Hence, the need for a decision support model which is both robust and user-friendly for modeling uncertain incident information, and for analyzing and suggesting control strategies, while respecting service quality requirements and operational constraints [SOU 00, HAY 03]. The decision support model should:

• – assist regulators in the control of regulation;
• – ensure connections in the best possible conditions;
• – avoid excessive customer waiting;
• – increase the attractiveness towards public transport while letting the regulator judge autonomously about the pertinence of the suggested regulation strategies.

7.4.1. Knowledge acquisition

The first concern during this research process even before the modeling formalism was how to elicit the knowledge of experts, who were in this case transport regulators and traffic operational managers in general. An endeavor demanding so much preciseness, such as knowledge acquisition, may take weeks or even months.

Furthermore, given the complexity of expert knowledge (it sometimes becomes difficult to make their mental processes explicit), acquired knowledge may be inaccurate, incomplete or even inconsistent.

The proposed approach to knowledge acquisition is based on information gathering techniques via the methods mentioned in the second chapter, such as interviews with experts and regulation operational managers. Following the multiple collaboration sessions held with them on several occasions, and the working days spent with them, and after the classification of this knowledge, criteria involved in the development of traffic control strategies were selected, among which are:

• – the state of each of the buses during connection (ahead of time/delayed);
• – the flow of passengers in each of the connection buses (negligible, medium or large);
• – the moment of the day reflecting traffic conditions (rush hour, end of the evening, etc.);
• – the availability of reserve means of transport;
• – the availability of human resources;
• – the type of incident that caused traffic problems.

Seeking to grant the reliability and robustness of the fuzzy decision support model for knowledge acquisition, a knowledge structuring and filtration process should be carried out in the following way:

• – structured interviews with the decision-makers. During the various missions carried out in Montbéliard, the authors of the chapter met with the regulators and the network experts on many occasions, organizing working and consultation meetings. They observed them during the fulfillment of their regulatory function. Such meetings with the regulators had been previously prepared in the form of questionnaires and diagrams in accordance with modeling constraints. The questions mainly focused on the regulatory strategies and the different scenarios which occur on a regular basis, as well as on the way of communicating with the drivers, key parameters and operational constraints. These interviews contributed to the extraction of knowledge related to the field under study (specific vocabulary, documentation, concepts, etc.) and to the acquisition of the know-how of regulators and traffic control experts;
• – the analysis of decision-maker verbal protocols. In order to identify and model the behavior of the decision-maker (the regulator in this case), a careful analysis of verbalization was carried out during the resolution of a real-life regulation case. Several simulation scenarios were set up and regulators were invited to express all their thoughts, whether fragmentary or trivial, without commenting on them;
• – classificatory expertise. This was the task that required spending the longest consultation time with the experts. Classification is an important activity in the transfer of expertise. The tools related to classification make it possible to obtain very precise and complete information, and serve as a means for controlling expert validity and thoroughness. Very fine grain knowledge is obtained and it is easier to encode. After “filtering” the knowledge of experts and regulators, the collection of expert knowledge is classified into two phases:
• – information collection;
• – information processing;
• – summarizing the expertise of decision-makers. Following the previous steps, a great amount of knowledge was acquired. However, this mass of knowledge could not always reflect the direct and integral communication of knowledge. Indeed, some of the actions performed by the regulator were unconscious, totally automatic and impossible to verbalize. Furthermore, as noted previously, an interview is a slow process whose profitability rapidly decreases in the measure that the research project progresses. This is why a synthesis of this expertise was carried out in consultation with all the experts in order to achieve and fulfill it in a rigorous and effective way.

7.4.2. Decision criteria for the regulation of public transport traffic

In order to structure the complex and heterogeneous mass of information concerning potential actions, it is necessary to identify the various points of view (aspects, characteristics or attributes regarding possible decisions) which may play a role in reasoning and establishing judgments. Thanks to this, it is possible to describe and to model the consequences of the implementation of potential actions, in relation to all the points of view deemed relevant.

Once formalized, the consequences of the actions do not always make it possible to directly translate the preferred judgment. Thus, taking into account one attribute for reasoning will only become effective when the decision-maker and/or all the actors concerned have agreed to hierarchize it according to relevant criteria.

There is a more general alternative for modeling preferences for a particular attribute. For this, it is necessary to build a set of attributes having a value function within the interval [0, 1] reflecting the degree to which a given consequence corresponds to the aspirations of the individuals involved.

Thanks to such techniques, it is possible to build a criterion, that is, a preference model related to a consequence or a set of consequences, which can be grouped along the same axis of synthetic meaning clearly perceived by the different actors. Formally, let us call a criterion any f(x) function defined on A, with a certain value within set E equipped with the relation of total order ≥_E, making it possible to compare performance, depending on the following relation:

R_f is a binary relation defined on A. The element E written as f(a) is classically called performance of a on criterion f.

For the purposes of regulation (set of maneuvers), the proposed approach resorts to a broad palette of more or less subjective criteria. The criteria listed below are the result of several collaborative sessions with regulators by applying the aforementioned knowledge acquisition techniques. Important criteria are, for example:

• – characteristics of buses during connection (ahead of time/delayed, load, coming/going, intervals, speed);
• – number of travelers;
• – timing of day, month and year (traffic);
• – regular or occasional events;
• – team change-off
• Secondary criteria can be:
• – type of traveler;
• – type of stations;
• – reserve means;
• – type of incident (regular or occasional).

All these criteria have to be taken into account in the decision support system, despite the fact that some may seem empirical. The proposed approach will also take into account the following criteria:

• – passenger waiting time, characterizing their comfort;
• – impact of regulation on the future evolution of the network and on the following connections;
• – cost of regulation: the regulations of the DSS will then favor the improvement of the service quality of connections.

7.4.3. Criteria modeling

Some system processors require the intervention of human interlocutors, either observers or potential users. These interlocutors introduce subjective descriptions, which imply the presence of the five human senses. In this case, granting reliability and obtaining results which are untainted by inaccuracies is a difficult task, and demands the use of specific knowledge modeling approaches.

The practical accomplishment of a decision support system necessarily implies knowledge acquisition. The simultaneous processing of this knowledge, which may be uncertain, can be achieved in a simpler manner using fuzzy subset theory.

This theory is also applied when:

• – there is non-numerical uncertainty about certain types of knowledge, reflecting the reliability of the actor providing it, or difficulties in observation;
• – there are classes with ill-defined borders, whose boundaries cannot be precisely indicated, or which are wrongly separated, with partially overlapping categories;
• – there is a search for flexibility in the accuracy of representation, or in the adaptation to a system’s conditions of use.

In other cases, fuzzy subset theory is used because it is easier to implement than traditional methods, and yields comparable results.

Criteria modeling through fuzzy subset theory is done through fuzzification that the interface establishes between the physical world and the data used by the model, playing the role of a digital/linguistic converter. It models and represents information through membership functions, in this case, by converting the available features in the operating system into linguistic terms defined by membership functions.

7.4.4. The fuzzification process

This process includes different phases:

• – from the available data, the fuzzification makes it possible to define types of situations (Figure 7.4);
• – thanks to an inference engine (regulation base), a type of decision can be associated with each type of situation;
• – defuzzification makes it possible to determine which is the exact decision that should be made.

Figure 7.5 gives an example of a fuzzification process for the advance–delay of the connecting buses. Being ahead of time/delayed is defined as the difference between the actual time of passage and the theoretical time. This is represented by fuzzy subsets and the following linguistic terms: very late, late, ahead of time.

Following the various collaborative sessions with the operational managers of the transport network, as well as with the regulators, and counting upon the expertise elicited during the knowledge acquisition phase, it is possible to define the time frames concerning the state of each of the connecting buses. These frames represent the time intervals or fuzzy subsets used in fuzzy inference through decision rules. When the time frame of one bus moves from one fuzzy subset to another, this results in a new situation and, as a result, fuzzy inference may result in a new type of decision. Thus, the subset “VL” reflects a situation in which the delay of the bus is excessive, and the subset “L” reflects a situation in which the delay of the bus is average, etc.

Another example is the process of fuzzification of the passenger flow, shown in Figure 7.6. The number of travelers is represented by the following fuzzy subsets and linguistic terms: negligible, medium, important. This is the number of travelers who are planning to make a connection.

The number of travelers is also a key parameter for regulation strategies. In the case where there are no customers planning to make a connection, the control action is not essential, but if it is not performed, it may cause problems in another point of the network. In this view, fuzzy subsets represent the value ranges which were key to changes in the regulation strategy. Thus, the large fuzzy subset reflects a situation in which the number of passengers is considerable, resulting in a very particular regulation strategy through the decision rules used in the fuzzy inference.

The process of fuzzification for the time of day is illustrated in Figure 7.7. The time of day reflecting traffic conditions is represented by the following fuzzy subsets and linguistic terms: off-peak hour, rush hour, team change-off time, late evening.

The time of day is a parameter reflecting traffic conditions. This information is as important as the one that was just identified in order to decide on the nature of the regulatory action. During the rush hour the traffic conditions are quite different from those at 3 pm, for example. For this, based on the expertise of regulators, fuzzy subsets were defined so as to model the information which influences the way in which human operators manage and regulate the transport network. At around noon, for example, the managers plan changing drivers, that is, the “team change-off”; this timing is modeled by using the fuzzy subset “CT”: team change-off time.

Figure 7.8 illustrates the fuzzification of proposals on the slowdown of buses. The proposals regarding the status of the bus refer to slowdown values for the regulation of traffic in the transport network when an incident disrupts the network. These are modeled by fuzzification through membership functions.

7.4.5. Generation of decisions

The generation of decisions for regulating traffic is the most important step in the approach. In fact, it reflects the automation of the regulators’ thinking process, reasoning and action, by drawing on their expertise and taking into account all the criteria which are decisive in regulatory strategies. It is the result of the fuzzy inference process after having synthetized the expertise obtained during the consultation with all the experts, in order to round it up and complete it in a rigorous and efficient way. The rules are expressed as follows:

• – example no. 1: “if the arriving bus is very late, and the departing bus is very late, and the number of travelers is negligible, and the time of day is the rush hour, then the slowdown is negligible”;
• – example no. 2: “if the arriving bus is very late, and the departing bus is late, and the number of travelers is average, and the time of day is the late evening, then the slowdown is average”;
• – example no. 3: “if the arriving bus is very late, and the departing bus is late, and the number of travelers is average, and the time of day is the team change-off time, then the slowdown is weak”;
• – example no. 4: “if the arriving bus is very late, and the departing bus is ahead of time, and the number of travelers is average, and the time of day is the rush hour, then the slowdown is important.

The slowdown always refers to the most advanced bus, all the rules are grouped in matrices in Table 7.1.

7.4.6. Defuzzification

The defuzzification process performs the opposite action to fuzzification, playing the role of a linguistic/digital converter. It turns the information in the form of subsets from the universe of state descriptions, deriving from the inference process, into numerical variables (Figure 7.9).

The defuzzification method used for the different membership functions of the chosen criteria represents the center of gravity method. This is the most accurate method, and is often used in similar cases to that of the regulation of urban transport traffic. However, it requires an integral calculation. The advantage of this method is that it takes into account all the available information and offers a very satisfying result. The formula is given in the following form (Figure 7.10):

In the decision support system for the regulation of connecting urban transport traffic, the discourse universe and the slowing down or waiting time value for connecting buses are the result of the difference between the theoretical time and the practical time.

Four examples are now introduced. For the first one, only one rule is activated, two are activated for the second one, four for the third and 16 for the last one, representing one of the most complex occurrences that may appear.

For example no. 1, we have the following vector:

The rule used in Figure 7.11 is: “if the arriving bus is late, and the departing bus is ahead of time, and the number of passengers is average, and the timing of day is the rush hour, then the slowdown of the departing bus is average”

For example no. 2, we have the following vector:

The rules used in Figure 7.12 are as follows:

• – the rule used in Figure 7.12-1 is: “if the arriving bus is very late, and the departing bus is ahead of time, and the number of passengers is average and the timing of day is rush hour, then the slowdown of the departing bus is important”;
• – the rule used in Figure 7.12-2 is: “if the arriving bus is very late, and the departing bus is late, and the number of passengers is average, and the timing of day is the rush hour, then the departing bus slowdown is average”.

This value, determined by defuzzification, represents the slowdown or waiting value of the bus ahead of time compared to the other connecting buses.

For example no. 3, we have the following vector:

The rules used in Figure 7.13 are as follows:

• – the rule used in Figure 7.13-1 is: “if the arriving bus is late, and the departing bus is ahead of time, and the number of travelers is average, and the time of day is the rush hour, then the departing bus slowdown is average”;
• – the rule used in Figure 7.13-2 is: “if the arriving bus is late, and the departing bus is late, and the number of passengers is average, and the time of day is the rush hour, then the departing bus slowdown is weak”;
• – the rule used in Figure 7.13-3 is: “if the arriving bus is late, and the departing bus is ahead of time, and the number of travelers is average, and the time of day is the off-peak time, then the slowdown of the departing bus is weak”;
• – the rule used in Figure 7.13-4 is: “if the arriving bus is late, and the departing bus is late, and the number of passengers is average and the time of day is the off-peak time, then the departing bus slowdown is weak”.

Finally, in example no. 4, we have the following vector:

As examples, here are some rule illustrations in Figure 7.14:

• – the rule used in Figure 7.14-1 is: “if the arriving bus is late, and the departing bus is very late, and the number of passengers is average, and the time of day is the off-peak time, then the departing bus slowdown is weak”;
• – the rule used in Figure 7.14-2 is: “if the arriving bus is late and the departing bus is late, and the number of passengers is average, and the time of day is the off-peak time, then the departing bus slowdown is weak”;
• – the rule used in Figure 7.14-3 is: “if the arriving bus is ahead of time, and the departing bus is very late, and the number of travelers is average, and the time of the day is the off-peak time, then the slowdown of the departing bus is average”;
• – the rule used in Figure 7.14-4 is: “if the arriving bus is ahead of time, and the departing bus is late, and the number of travelers is average, and the time of day is the off-peak time, then the slowdown of the departing bus is weak”;
• – the rule used in Figure 7.14-5 is: “if the arriving bus is late and the departing bus is very late, and the number of passengers is important, and the time of day is the off-peak time, then the slowdown of the departing bus is weak”;
• – and so on until the last rule in Figure 7.14-16 which is: “if the arriving bus is ahead of time and the departing bus is late, and the number of passengers is important, and the time of day is the late evening, then the slowdown of the departing bus is average”.

7.4.7. Types of decisions

Given the large number of scenarios and of inferences the system may encounter, a classification of the regulation strategies into types of decisions is proposed according to the value found after defuzzification.

7.4.7.3. “C”-type regulatory decision

From the moment the slowdown becomes very important, neither the slowdown nor the wait are possible. In this case, the type of decision will embrace several regulation strategies.

7.4.7.3.1 Substitution

Substitution refers to the action of substituting one of the cars for the intended service. This maneuver involves using an available car (parked, re-entering, delayed) to replace the one originally planned and which became unavailable (breakdown, late arrival, etc.). The machinist chooses a parked car or picks up a car on its way back, or a delayed car, and sets out to the place where the incident was reported. The change-off service will park its car on arrival at the terminus or relay it to the incoming service, or to the delayed service. Figure 7.15 is an example of this type of maneuver being two stations: Temple and Acropole.

Car 01 blocked in station Z will be late for the connection at 12:40. Car 05 is moved in order to ensure this departure and replace Car 01. The machinist of Car 01 will park the car on arrival at Temple.

7.4.7.3.2 Transshipment

Transshipment is the transfer of passengers from one car to another. It is planned when substitution is not possible due to a lack of available means. This maneuver makes it possible to relieve a car from its charge. It is essential in the case of car unavailability on a line. Furthermore, it makes other maneuvers such as turning around possible. In order to carry out a transshipment, it is necessary to have at least two grouped cars. The number of grouped cars, the load of each of them, the number of passengers to be removed in the section after the transshipment point are all factors which determine the feasibility of the maneuver. The need for a car near the transshipment points (load in the opposite direction, gaps to be filled) and the constraints of the staff management (team change-off to be granted) determine the relevance of this decision.

For instance, in Figure 7.16, car no. 1 is very late due to a breakdown. The decision is to transship the passengers of car no. 1 to car no. 2.

7.4.7.3.3 Assuring the connection at another meeting point

This maneuver refers to postponing the departure of a bus to another stopping point. It makes it possible to re-establish the regularity of the traffic and the absorption of an irregular charge: the car is then delayed at the meeting point in question, that is, the time required for the late bus to reach the stop and the passengers to climb into the car. For example, in Figure 7.17, bus line 3 has fallen behind and will therefore not be able to reach the Temple connection node with bus line 2. However, the two lines 3 and 2 will meet again at Acropolis and will be able to ensure the connection.

7.4.8. Suggestions of regulatory strategies

After detecting the incidents of transport connections in related to the operation feedback, the DSS carries out the process of problem-solving and infers which decisions or regulation strategies can be suggested. This proposal is materialized in the maneuvers that each of the connecting bus drivers must perform. These maneuvers seek to coordinate the buses at the connection stage, acknowledging various sources of information, such as incident reports (mechanical breakdown, traffic jams, etc.), or the creation of connections, which are simulated in order to assess their impact on the connection, and therefore make it possible to decide on the validity of the decision.

7.4.9. Impact and validation of regulatory strategies

Although they take into account all the experimental and practical theoretical knowledge of the experts of the transport networks and regulators, decisions or suggested strategies are subject to validation by regulators in the sense that this is a decision support tool. They are evaluated on the basis of significant criteria such as:

• – evolution of the bus being ahead of time/delayed;
• – successful connection;
• – regulatory cost;
• – passenger waiting time.

These criteria, as others that the operator recommends, enable regulators to decide on the validity of these regulatory strategies and to classify them in view of defining the best solutions for traffic regulation. For this, after the proposal takes place, an immediate and automatic simulation should be implemented in order to reveal the impact of these strategies on the network in real time. In the case where the suggested strategies do not fulfill the best conditions for traffic regulation or do not completely satisfy the requirements set by the managers, after a technical or material incident has taken place, regulators may suggest other strategies that will be integrated into the knowledge base for use in future configurations.

7.4.10. Implementation of regulatory strategies

Once validated, the regulatory strategies are stored for future uses and implemented on the transport network. Throughout their application a simulation must be performed in order to measure and study the impact of the regulation on the network in a theoretical way and to be able to make a comparison with practical study cases. These measurements and evaluations become the subject of statistics for the future perspectives of operators, particularly in relation to:

• – the architecture and organization of the network;
• – the management strategy of the operation;
• – the arrangement of connections or other operation-related perspectives.