Analyzing Demand with Respect to Offer of Mobility
1.1. Related Works
1.2. Paper Aims and Structure
- investigate how the demand is satisfied in terms of public transportation mobility by the offer considering model, simulation tools and KPIs (key performance indicator, e.g., drop-offs and pick-ups at a stop) and related evidence information (e.g., passing lines in every time interval, passing lines in each time interval) with the aim of identifying crowded condition on bus-stops and on the busses.
- Taking care in the model: (i) a range of data sources. People flow depends on different aspects (e.g., the time of day, the motivation of people for travelling, the structure public transportation network, places that with high impacts on mobility patterns). Therefore, using a wide spectrum of data sources related to the city structure (e.g., roads, stop locations), the mobility demand considering different places (e.g., residential areas, shopping centers, stadiums), and the service offer (e.g., the number of vehicle trips and lines passing the stops) supports a deeper analysis of PTSs, and as a result, more precise decisions about mobility policies; (ii) multi-modality. Multiple travel modes which permit to consider different public transportation modalities; (iii) multi-operators. It provides the analysis of the transportation network when there are multiple mobility operators; (iv) KPIs to objectively assess the alternative scenarios and making a (set of) decision(s) to improve the service offer considering the mobility demand.
- perform what-if analysis by analyzing the impact of a (set of) change(s) in Actual Scenario to see their effects in terms of people and matching demand/offer.
2. Data Source and Requirements Analysis
- census data which includes the mobility (and the public transportation means) of residents from/to the city for different purposes (e.g., work, study);
- traffic flow which includes counting (private) vehicles entering or exiting from the city over time which is typically performed on the city border using different methods (e.g., plate number recognition);
- city structure and services attract city users depending on daytime. For example, in the morning, residential areas and public parking spaces can be starting point of trips because the city users usually start from these places to go to work and study places and return in the late afternoon. Likewise, in the afternoon, service providers (e.g., offices, stores, industries, schools, shopping malls, touristic places).
- commuter flows in the city which can be obtained using different data sources (e.g., Wi-Fi, network cellular, mobile applications, PAXCounter);
- commuter flows accessing the city. For example, in cities (e.g., Venezia, Roma, Firenze) that the presence of tourists cannot be neglected compared to others (e.g., workers, students), data that come from cellular  or Wi-Fi  networks, can help to analyze different aspects (e.g., the number of tourists that are daily present in the city, the duration of their stay, their origin and destination in long term/distance).
- deployed transportation services which can be achieved using different methods (e.g., counting the commuters onboard, waiting at stops/stations, in multimodal hubs, exiting from railway stations over time).
3. DORAM Architecture
- Scenario Production. A new scenario is created by aggregating different kind of data. The Scenario model is formalized in a knowledge base NoSQL RDF Storage with all the city relationships and services are modeled. In particular: (i) to load/change/create a transport offer in the so called static GTFS manager (GTFS) has been used to edit an existing GTFS data feed, (ii) Snap4City OSM2SM tool allowed to retrieve the residential building data from OpenStreetMap (OSM)  database and save them in a PostgreSQL database, (iii) census data, which includes different categories (e.g., daily resident flow from each locality to another for work or study, the daily tourist flow), retrieved from various sources (e.g., [41,42]), (iv) Points Of Interest (POI) data, retrieved using ServiceMap of Snap4City .
- DORAM Modelling takes the scenario information to define the simulation and relating the data, associated with service offer and the mobility demand, and the simulation output, as described in Section 4.
- DORAM Analyzer performs the analysis of the public transportation system by comparing the mobility demand and service offer. In order to do that, the scenario data are retrieved using ServiceMap APIs calls and SPARQL queries performed on Snap4City/Km4City knowledge base  provided for each scenario in a separated Docker (e.g., [41,42]).
- DORAM Front-End allows to (i) select the scenario to be analyzed, (ii) browse the scenario by navigating in the transport offer (bus stops, lines, time schedule, start stops, etc.), (iii) browse the city areas to see the services and point of interests, (iv) analyze the results of each scenario.
4. Model and Analyzer
4.1. Modelling Public Transport Service Offer
4.2. Modelling Mobility Demand
4.3. Service Offer with Respect Mobility Demand Computation
4.3.1. Paper Aims and Structure
4.3.2. Beginning and Final Stops’ Analysis
4.3.3. KPI Analysis
5. DORAM Tool
- Multi-modality: transportation modalities are considered in the model: train, tram, and bus;
- Multi-operativity: It manages the offers of different mobility operators. In the city of Florence, we have 13 main mobility operators (e.g., ATAF, Trenitalia, GEST, etc.). The distribution of their services is not uniform.
- changing the offer and the demand in the scenarios of analysis by creating new scenarios, changing the offer and also the services in the maps, and knowledgebase. More details are reported in Section 6.
- computing KPIs and related evidence information to perform the PTS analysis and provide support for decision makers about mobility policies in the city. The most relevant KPIs include the number pick-ups and drop-offs at a given stop over time. Moreover, nearby POIs (shopping centers, offices, shops, tourist attractions) along with each vehicle line, the number of vehicle lines and trips in each time interval are the examples of provided information to support the scenario analysis.
6. Scenarios Analysis and Results
6.1. Validation of the DORAM Model and Weights
6.2. Alternative Scenario Analysis
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
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|Selected Tools at the State of the Art||Web-Based Interface That Supports GTFS Data and REST APIs for Analysis Results||Multi-Modal Mobility Application||Multiple Mobility Operators Applications||What-If Analysis Performance|
|A given bus stop|
|A given time in|
|Vehicle capacity with modality m and mobility operator|
|Offer by means ODMs|
|Demand by means ODMs|
|Set of locations in the region R|
|Set of locations in the area A|
|Number of inbound and outbound individual trips|
|Beginning, Transfer and Final stops in a commuter trip|
|Circle with stop s in its center and related radius r|
|Modality weight vector|
|pr (parking, residential buildings) weight vector|
|Service provider weight vector|
|Stop level of stop s in interval time|
|Line level of stop s in interval time|
|pr (parking, residential buildings) level of stop s|
|Service provider level of stop s|
|stop weighted sum of stop s in interval time|
|pr (parking, residential buildings) weighted sum of stop s|
|Service provider weighted sum of stop s|
|Probability that stop s is a transfer one by choosing other stops in circle c(s,r) in interval time|
|Transfer Probability of the stop s by choosing other lines of s in interval time|
|Transfer Probability of the stop s in interval time|
|Beginning Probability of the stop s in t ∈ T|
|Final Probability of the stop s in|
|Drop-offs and pick-ups at stop s in interval time|
|Number of vehicle arrivals at stop s in time interval|
|Feature||ATAF Dataset||Florence (Total)|
|Service time span||(next day)||(next day)|
|Afternoon time||(next day)|
|Time interval size|
|Radius r around stop s|
|Whole ATAF dataset||Max.||59||59|
|Removing 1% of outliers in ATAF dataset||Max.||10||10|
|Stop||Indipendenza XXVII Aprile||Indipendenza XXVII Aprile||Cosimo Ridolfi|
|Daily pick-ups||compared to Actual Scenario)|
|Daily drop-offs||compared to Actual Scenario)|
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Arman, A.; Badii, C.; Bellini, P.; Bilotta, S.; Nesi, P.; Paolucci, M. Analyzing Demand with Respect to Offer of Mobility. Appl. Sci. 2022, 12, 8982. https://doi.org/10.3390/app12188982
Arman A, Badii C, Bellini P, Bilotta S, Nesi P, Paolucci M. Analyzing Demand with Respect to Offer of Mobility. Applied Sciences. 2022; 12(18):8982. https://doi.org/10.3390/app12188982Chicago/Turabian Style
Arman, Ala, Claudio Badii, Pierfrancesco Bellini, Stefano Bilotta, Paolo Nesi, and Michela Paolucci. 2022. "Analyzing Demand with Respect to Offer of Mobility" Applied Sciences 12, no. 18: 8982. https://doi.org/10.3390/app12188982