# Analyzing Demand with Respect to Offer of Mobility
### Related Works  
The analysis and simulation of people flow in smart cities attracts attention due to related applications in urban mobility (e.g., [17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]). For example, in [17], the tracking travellers' data are collected by means of public transport IC cards and they have been modelled to predict human mobility, using three approaches. In the first two, it is assumed that the probability of passengers' drop-off on bus line stops follows a standard or a uniform normal distribution (quite unrealisible). In the third approach, the stops have been labelled as \"large\", \"medium\" and \"small\", with respect to the numbers of passengers that are exchanged. Then, the probability of drop-off at a stop has been considered following the labelling strategy, based on the idea that larger stops attract more passengers, and as a result, the probability of drop-off at them is higher. A model for analyzing the dwell time of a bus, which is the time where a bus is stopped at a stop to exchange passengers, has been proposed in [18]. For this, the boarding and the alighting behavior of passengers, considering respectively time intervals between successive records of tapping-ins and tapping-offs, are investigated. The idea of using tapping-off information may not be applicable when onboard passengers are not obliged to tap off when they disembark at the stop. A method for the evaluation of the quality of stop alighting and boarding has been presented in [19]. One part of the mentioned research focuses on predicting the alighting stops of a stage considering a multiple-stage trip by finding the closest stop to the boarding stop of the next trip stage. A method to estimate passenger flow in real-time modality for bus transit urban systems has been proposed in [20]. In this case, the number of people picked up at a station with a smart card is estimated according to two consecutive taping records. Then, using regular commute patterns, the passengers' alighting stations are estimated. Finally, after an estimation of the passengers' number in real-time on a bus trip, the evaluation of onboard passengers' number is considered by using Kalman. Otherwise, real-time mobility demand fluctuations by adopting schedule adjustments including, holding, and speed changing, has been studied in [21]. Based on a combination of big data sources and primary data, a methodology to evaluate the total trips amount to be reallocated, while considering passenger waiting time and trip cost, has been presented in [22]. A non-linear optimization model to optimize train timetables by analyzing the actual passenger loading time periods and waiting times, when records from fare collection systems are considered as a data source, has been proposed in [23]. Innovative solutions related to customized busses (in China) are analyzed in [24]. In this case, the demand estimation is performed by using the information provided by users via online platforms, in a dynamic interactive manner between operators and users, while feedbacks are exchanged among them in each step. A scheduling model to generate a timetable by realizing tradeoffs among the train utilization, waiting time, energy consumption, and service levels, has been proposed in [25]. A model to estimate suitable elapsed time thresholds between sequential trip stages to identify transfers using onboard sensors, smart card fare payment data, and intermittent surveys by mobility operators is considered in [26]. A methodology to analyze comfort in PTSs, considering acceleration and vehicle vibration effects (based on international standard ISO standards) has been proposed in [27]. A visual interactive analytics system, called MobiSeg, has been proposed to support the movement activities exploration of people by analyzing taxi trajectories, telco data, and metro passenger RFID card data in [28]. There are a few works in the literature focusing on analyzing the impact of changes made in people flow (e.g., [15; 29]).