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Commercial bus speed is a key factor in the operation of public transport systems because it represents a direct measure of the quality of service provided to users and also considerably affects system costs. By commercial speed, we are referring to the average speed of buses over stretches, including all operational stops. Evaluating system perfor...
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Context 1
... important issue is the projection of the GPS locations onto the path definitions. These points were properly rectified using the methodology described previously (computing the orthogonal projection of the GPS points into the path line that is defined by the segmentation points). In Figure 2, we show the projection process in the following two interesting cases: a straight path and a 90-degree turn. In Figure 2, each pair ! ! denotes the position (in two-dimensional coordinates) that is instantly recorded by a GPS pulse i . The pair ( d ! , t ! ) corresponds to the new description of a GPS pulse i in which the position is translated into a distance measurement relative to the origin of the pulses’ associated route path. The computation of a representative speed for the Transantiago buses is a major challenge, mainly due to the amount of information and the degree of data disaggregation. To obtain reliable speed values, a crucial issue is the proper management, processing and estimation of the required inputs for the calculations (distance and lapses based on interpolations in time and ...
Context 2
... important issue is the projection of the GPS locations onto the path definitions. These points were properly rectified using the methodology described previously (computing the orthogonal projection of the GPS points into the path line that is defined by the segmentation points). In Figure 2, we show the projection process in the following two interesting cases: a straight path and a 90-degree turn. In Figure 2, each pair ! ! denotes the position (in two-dimensional coordinates) that is instantly recorded by a GPS pulse i . The pair ( d ! , t ! ) corresponds to the new description of a GPS pulse i in which the position is translated into a distance measurement relative to the origin of the pulses’ associated route path. The computation of a representative speed for the Transantiago buses is a major challenge, mainly due to the amount of information and the degree of data disaggregation. To obtain reliable speed values, a crucial issue is the proper management, processing and estimation of the required inputs for the calculations (distance and lapses based on interpolations in time and ...
Context 3
... important issue is the projection of the GPS locations onto the path definitions. These points were properly rectified using the methodology described previously (computing the orthogonal projection of the GPS points into the path line that is defined by the segmentation points). In Fig. 2, we show the projection process in the following two interesting cases: a straight path and a 90° turn. In Fig. ...
Context 4
... onto the path definitions. These points were properly rectified using the methodology described previously (computing the orthogonal projection of the GPS points into the path line that is defined by the segmentation points). In Fig. 2, we show the projection process in the following two interesting cases: a straight path and a 90° turn. In Fig. ...
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Citations
... Running speed and commercial speed represent two critical categories of operating speeds for vehicles traversing a route. The distinction between these speeds has been outlined in [44], with [45] contributing a model for calculating the commercial speed of buses along a route. Additionally, ref. [46] has identified the average speed for various transportation modes. ...
The Mediterranean region is a hot spot for climate change, with transportation accounting for a quarter of global CO2 emissions. To meet the 2030 Sustainable Development Goals (SDGs), a sustainable urban transport network is needed to cut carbon emissions and improve air quality. This study aims to investigate the electrification of public transport in both developed and underdeveloped countries by examining the existing public transport network of two modes of transportation (buses and trams) across the Mediterranean region. This study suggests that the electrification of public transportation could result in a significant additional demand for more than 200 GWh of electricity, depending on the size and congestion of the city. It also studies the potential reduction of greenhouse gas (GHG) emissions through the electrification of buses. Results show that electrification significantly impacts decreasing GHG emissions, helping achieve SDG 13. Furthermore, a financial analysis was conducted to determine the feasibility of using different bus fuel technologies. Regarding economic benefits, electric buses are not consistently optimal solutions, and diesel buses can be advantageous. Our finding shows that, at a 5% discount rate, the diesel bus is most favorable for Marseille, and, as discount rates increase, the advantage of electric buses diminishes. However, the high purchase price of electric buses compared to diesel buses is currently a major obstacle in achieving SDG 11, particularly for developing countries.
... We propose a modeling framework that aims to provide a versatile MaaS system wherein users' trip requests are represented in terms of mode-agnostic mobility resources. In the proposed MaaS system, the average speed of travel modes is assumed to be known and representative of the commercial speed of each mode, defined as the average speed that incorporates service delays, e.g., waiting time, transfer time, and operational stop time into the average driving speed (Cortés et al., 2011). Commercial speed is a key factor in the operation of public transport systems since it represents a direct measure of the quality of service and also considerably affects system costs. ...
In the context of Mobility-as-a-Service (MaaS), the transportation sector has been evolving towards user-centric business models, which put the user experience and tailored mobility solutions at the center of the offer. The emerging concept of MaaS emphasizes that users value experience-relevant factors, e.g., service time, inconvenience cost, and travel delay, over segmented travel modes choices. This study proposes an auction-based mechanism and tractable optimization models for the demand-side management of MaaS systems wherein users’ trip requests are represented as mode-agnostic mobility resources. Users’ requests arrive dynamically in the MaaS system and users compete for mobility resources by bidding for mobility services based on their willingness to pay and experience-relevant preferences. We take the perspective of a MaaS platform regulator who aims to maximize social welfare by optimally allocating mobility resources to users in real-time. The MaaS regulator first decides whether to offer each user a MaaS bundle and identifies the optimal allocation of mobility resources for the selected users. Users have the possibility to accept or reject offered MaaS bundles by comparing the associated utility obtained from MaaS with a reserve utility obtained from other travel options. We introduce mixed-integer programming formulations for this online mobility resource allocation problem. We show that the proposed MaaS mechanism is incentive-compatible, individually rational, budget balanced, and computationally efficient. We propose a polynomial-time online algorithm and derive its competitive ratio relative to an offline algorithm. We also explore rolling horizon configurations with varying look-ahead policies to implement the proposed mechanism. Extensive numerical simulations conducted on large-scale instances generated from realistic mobility data highlight the benefits of the proposed mechanism.
... In public bus transportation networks, bus speed is considered to be a key performance indicator (KPI) that translates the level of efficacy and attractiveness of a bus network [7][8][9] Consequently, bus networks operators struggle daily to maintain high bus speed and are in need of reliable, complete, and flexible prospective methods to predict the performance (such as speed) of future bus lines. ...
Models at runtime have been initially investigated for adaptive systems. Models are used as a reflective layer of the current state of the system to support the implementation of a feedback loop. More recently, models at runtime have also been identified as key for supporting the development of full-fledged digital twins. However, this use of models at runtime raises new challenges, such as the ability to seamlessly interact with the past, present, and future states of the system. In this paper, we propose a framework called DataTime to implement models at runtime which capture the state of the system according to the dimensions of both time and space, here modeled as a directed graph where both nodes and edges bear local states (i.e., values of properties of interest). DataTime offers a unifying interface to query the past, present, and future (predicted) states of the system. This unifying interface provides (i) an optimized structure of the time series that capture the past states of the system, possibly evolving over time, (ii) the ability to get the last available value provided by the system’s sensors, and (iii) a continuous micro-learning over graph edges of a predictive model to make it possible to query future states, either locally or more globally, thanks to a composition law. The framework has been developed and evaluated in the context of the Intelligent Public Transportation Systems of the city of Rennes (France). This experimentation has demonstrated how DataTime can be used for managing data from the past, the present, and the future and facilitate the development of digital twins.
... A MaaS bundle is defined as a combination of service times across travel modes in the MaaS system. In this MaaS framework, the average speed of the travel modes is assumed to be known and representative of the "commercial speed" of each mode, defined as the average speed that incorporates service delays, e.g., waiting time, transfer time and operational stop time, into the average driving speed ( Cortés, Gibson, Gschwender, Munizaga, & Zúñiga, 2011 ). "Commercial speed" represents a direct measure of the quality of service and considerably affects system costs. ...
Mobility-as-a-Service (MaaS) is an emerging business model in transportation enabled through mobile internet technologies. A MaaS platform can be viewed as a two-sided market, where travelers and transportation service providers (TSPs) are two groups of interacting agents. We propose an optimization framework for the regulation of two-sided MaaS markets. We cast this problem as a single-leader multi-follower game (SLMFG) where the leader is the MaaS regulator and two groups of follower problems represent the travelers and the TSPs. The MaaS regulator aims to maximize its profits by optimizing service prices and resource allocation. In response, travelers (resp. TSPs) adjust their participation level in the MaaS platform to minimize their travel costs (resp. maximize their profits). We analyze network effects in the MaaS market and formulate SLMFGs without/with network effects leading to mixed-integer linear/quadratic bilevel programming problems. We propose single-level reformulations based on mathematical programming with equilibrium constraints (MPECs) and prove the equivalence between the solutions obtained using the MPECs and the original bilevel problems. Customized branch-and-bound algorithms based on strong duality reformulations are developed to solve these MPECs. Extensive numerical experiments conducted on large-scale instances generated from realistic mobility data highlight the performance of the proposed algorithms relative to a benchmarking approach, and provide meaningful managerial insights for the regulation of two-sided MaaS markets.
... In general, waiting times can be estimated from the cyclist's speed profile. Different approaches have been taken to calculate the most likely speed profile [12][13][14][15] and to estimate the trend of motion. For example, Strauss and Miranda-Moreno (2017) = approximated the speed profile by averaging over three, four, and seven GPS points before estimating the cyclist's speed and time-delay at intersections, which is different from waiting time. ...
This paper seeks to predict the average waiting time, defined as the time spent moving at 1 ms−1 or less, of urban bicyclists during rush hours while performing different maneuvers at intersections. Individual predictive models are built for the three cyclist typologies previously identified on a large database of GPS traces recorded in the city of Bologna, Italy. Individual models are built for the three cyclist typologies and bootstrapping has confirmed the validity and robustness of the results. The results allow the integration of waiting times in route choice models for cyclists, thus improving the rational bases by which cyclists makes their decisions. Moreover, the modeling allows transportation engineers to understand how different cyclist typologies perceive different variables that affect their waiting times. Future work should focus on testing the model transferability to other case studies.
... The speed of the buses was set to 12 km/h (considered the average speed of a standard bus on Labor Day), according to the Spanish association of managing companies for urban collective transport (Cortés et al., 2011). We are using a constant speed profile for the e Buses, the acceleration is therefore set to 0. As stated in Yu et al. (2016), the passenger mass only influences the bus energy consumption for speeds over 30 km/h. ...
... Moreover, in terms of the energy consumption, we are considering regenerative breaking when circulating downhill (see Eq. 13) with an average speed of 12 km/h (Cortés et al., 2011) and an overnight recharging strategy. ...
... For example, smartcard systems, on-board bus units (AVLs 1 ), schedule, referential, and real time radio bus monitoring systems are a gold mine to manage the bus network, useful to understand how it works and what to act on to improve it. For example, the average trip travel time, the schedule, the amount of km per year, the average bus stop spacing, etc. can be considered as inputs that, when properly processed, help the creation of service quality indicators for bus networks [5][6][7]. ...
Public Transportation Information Systems (PTIS) are widely used for public bus services amongst cities in the world. These systems gather information about trips, bus stops, bus speeds, ridership, etc. This massive data are an inviting source of information for machine learning predictive tools. However, it most often suffers from quality deficiencies, due to multiple data sets with multiple structures, to different infrastructures using incompatible technologies, to human errors or hardware failures. In this paper, we consider the impact of data cleansing on a classical machine-learning task: predicting urban bus commercial speed. We show that simple, transport specific business and quality rules can drastically enhance data quality, whereas more sophisticated rules may offer little improvements despite a high computational cost.
... For example, smartcard systems, on-board bus units (AVLs 1 ), schedule, referential, and real time radio bus monitoring systems are a gold mine to manage the bus network, useful to understand how it works and what to act on to improve it. For example, the average trip travel time, the schedule, the amount of km per year, the average bus stop spacing, etc. can be considered as inputs that, when properly processed, help the creation of service quality indicators for bus networks [23,15,14]. ...
... Commercial speed, that is the speed of the bus felt by the passengers while they travel, is among the major indicators used by operators to evaluate the efficiency of bus lines and assess the state of health and quality / attractiveness of a bus network. This measure is computed using the total travel time over any section on which the indicator has to be computed, including boarding and alighting time at bus stops, traffic perturbation, and any time related factors that impact the travel time, hence the speed of buses as stated by [23,55,32,44,14,15]. Studies have been made on what to act on to both understand and control bus commercial speed fluctuations. ...
... They assessed this impact in the city of Helsinki, Finland. Traffic influence on buses travel time have also be reported to be prominent, as mentioned by Mazloumi [44] and Cortes [14]. However, the gathering of valuable traffic data seems to be a possible challenge. ...
The so called data era we have entered in is accompanied by an explosion of data, both in variety and quantity. Public transportation is a data-intensive field, and related information systems are often supported by old technologies that struggle to keep up as the amount of data continually increases. This poses two problems. First, the massive data generated by the transportation network must be qualified and enriched with external data sources in order to be used for decision making. Second, in order to limit the number of tools and the complexity of maintenance, it is desirable to integrate data governance with decision support tools to allow non-expert operators to manipulate this data. Through four contributions leading to the proposal of a technical framework that integrates the past, present and future into a traditional information system containing a priori models, this thesis argues that the integration of various highly qualified datasets from the real world into a single spatio-temporal model provides a qualitative, efficient and low-cost mean of analysis, prediction and strategic decision support for bus networks while depreciating the use of data management systems in a non integrated multi-tool data management systems ?
... With travel speed, we can not only measure congestion but also derive the travel time and delay. The same holds true for the bus transit systems [1,2]. Predicting bus speeds in a timely and accurate manner contributes to maximizing the benefits of an intelligent public transit system [3]. ...
This study proposes a novel Bayesian hierarchical approach for online bus speed prediction by explicitly accounting for the spatiotemporal interaction (STI) of speed observations. The use of Laplace approximation can expedite the estimation of Bayesian models and enable the implementation of online prediction. Large numbers of trials are carried out to identify significant predictors and the optimal length of the look-back time window to achieve the highest prediction accuracy. The spatiotemporal interacting patterns are also explored, and results show that the Type IV model assuming the structured spatial effect interacts with the structured temporal effect can best accommodate the bus speed data. Besides, prediction errors of the Type IV model randomly distribute over time and space. The proposed model can achieve high prediction accuracy and computational efficiency without compromising the interpretability of the contributing factors and the unobserved spatiotemporal heterogeneity. The proposed model can be used to assist public transit operation and management, such as bus scheduling, congestion warning, and the development of proactive measures to mitigate bus delays.
... According to the latest Origin-Destination Survey from Santiago, in a normal working day, 34.5% of trips are made by walking, 25.7% by car, 24.2% by public transport (bus and Metro), 4% by cycling, 2.9% by shared taxi ("colectivos") and 1.7% by taxi (SECTRA, 2014;Muñoz et al., 2015). Santiago has been the subject of several transport studies, including operational, social and economic analyses of Santiago's public transport system (e.g., Cortés et al., 2011;Muñoz et al., 2014;Ureta, 2015;Batarce and Galilea, 2018), estimations of road traffic externalities (Rizzi and De La Maza, 2017) and optimal fuel tax (Parry and Strand, 2012), assessments of cycling infrastructure from the users' point-of-view (Rossetti et al., 2018) and of walking as the main mode for urban trips (Herrmann-Lunecke et al., 2020). In the past five years the arrival of ride-hailing apps (e.g., Uber, Cabify) has been a disruptive force in the transport system, with a continuous growth in the number of riders and drivers, that have resulted in an increase of traffic levels, mainly due to the attractiveness of ride-hailing as a substitute of public transport (Tirachini and del Río, 2019;Tirachini and Gomez-Lobo, 2020). ...
We propose a marginal tax reform model that includes both formal and informal sectors in the economy, traffic externalities (congestion, pollution, crashes and noise) and distributional concerns. The marginal cost of public funds (MCF) is derived for fuel taxes and public transport subsidies. The model is applied to Santiago, Chile. If MCFs are computed only taking economic efficiency into account, a revenue neutral reform within the transport sector suggests increasing the car cost and reducing the bus fare in peak periods, and reducing the car cost and increasing the bus fare in off-peak periods. However, including income inequality aversion leads to suggesting lower bus fares and higher car costs in both peak and off-peak periods, significantly changing the economic assessment of current tax and subsidy instruments. The inclusion of traffic externalities has a large effect on the MCF for fuel tax and a mild effect on MCF for bus subsidy.
