1. General Assembly 2017, Stockholm, Sweden
Development of predictive models for public transport operations to improve user information, indicators and real-time forecast for operators and transit agencies
motivacion
Ricardo Giesen, Juan Carlos Herrera, Hans-Albert Löbel, Felipe Delgado, ???
General Assembly 2017, Stockholm, Sweden
Aug 13, 2017

2. Outline Motivation: Improve Transit Agency/Operator Key Functions
Objectives
Next Steps

3. Key Transit Agency/Operator Functions
A. Off-Line Functions
Service and Operations Planning (SOP)
Performance Measurement (PM)
B. Real-Time Functions
Service and Operations Control and Management (SOCM)
Customer Information (CI)

4. System Monitoring, Analysis, and Prediction
Key Functions
Off-line Functions
Real-time Functions
Supply
Demand
Customer
Information (CI)
Service Management
(SOCM)
Service and Operations
Planning (SOP)
ADCS
Performance Measurement (PM)
System Monitoring, Analysis, and Prediction
ADCS: Automatic Data Collection Systems

5. Examples of ADCS in Decision Support
Passenger Flow and System Capacity
Public Transport OD Matrix Estimation
Performance Measurement (PM)
Real time demand estimation and control
Customer information

6. Commercial Dispatcher Console
Synoptic
Fleet control & monitoring
Commercial Dispatcher
Comply with frequency, regularity and indicators
Smart dispatcher
Console
Driver assistance
On-route regularity
Terminal Dispatcher
Opening & closing of driver shifts
Non commercial movements
Surveying
Passenger counting App
Demand
Per stop & line
Load profile
Fare evasion supervision
Users
Real-time updates to users
Training
Dispatchers & drivers

7. Motivation Benefits of the User Benefits of the Operator
Improvement of travel planning
Knowledge of travel times
Reduction of waiting anxiety (Peng et al., 2002)
cambiar
Benefits of the Operator
Better strategic planning of the routes and number of buses
Option to generate Priority Traffic Light Systems
Maintain bus regularity in passing frequency (Ex: control mechanism)

8. Main Objective
Development of predictive models that take advantage of multi-source heterogeneous data set to predict the state of public transport systems for short and medium term horizons.

9. Multi-source heterogeneous data sets
We are considering are:
Transactional data from fare card collection systems, automatic toll collection systems, accidents, etc.
Vehicle related data such as GPS tracks, accelerometer, gyro, thermometer, images, etc.
Publicly available data, such demographics, economics, social media, weather, incidents, etc.

10. Specific Objectives
Identify advantages and disadvantages of different data sources for building predictive models.
Provide and generate recommendation to users at individual level based on the status of the system and given their preferences.
Provide and generate recommendation to operators to offer a better service to their customers.

11. Methods
We consider the collection of multi-source data set to calibrate and then validate the proposed forecast methods.
We are considering the construction of an architecture based on a mixture of modern deep learning models to jointly process multi- source data from transactional, vehicles, and publicly available.
Propose optimization methods to take advantage of forecast developed in the project so that better decision can be made.

12. Next Steps: Improve Level of Service Prediction
Travel Speed
Crowdedness / Seat Availability
etc.
Transit Route Recommendation
… Looking for partners

13. General Assembly 2017, Stockholm, Sweden
Development of predictive models for public transport operations to improve user information, indicators and real-time forecast for operators and transit agencies
motivacion
Ricardo Giesen, Juan Carlos Herrera, Hans-Albert Löbel, Felipe Delgado, ???
General Assembly 2017, Stockholm, Sweden
Aug 13, 2017