Rafał Kucharski
Tadeusz Kościuszko University of Technology
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Featured researches published by Rafał Kucharski.
international conference on transport systems telematics | 2016
Rafał Kucharski; Guido Gentile
We propose the probabilistic information spread model to represent the spatiotemporal process of becoming aware while traversing the traffic network. In the contemporary traffic networks drivers are exposed to multiple traffic information sources simultaneously. Traffic managers look for a realistic estimate on when, where and how many drivers become informed about the actual traffic state (e.g. about the event). To this end we propose the probabilistic Information Spread Model (ISM) representing the process of spreading information to the drivers via multiple information sources (radio, VMS, on-line information, mobile applications, etc.). We express the probability of receiving information from a given information source using specifically defined spreading profile (formalized through the probability density function) and market penetration of respective source, with a novel information spreading model for on-line sources (websites, mobile apps, social networks etc.). Moreover, by assuming the information sources are mutually independent, the simplified formula for the joint probability can be used so that the model becomes practically applicable in real-time applications. Model is designed to work within the macroscopic dynamic traffic assignment (DTA) as a part of the network flow propagation model. Thanks to that, the informed drivers can be traced as they propagate through the network towards their destinations. We illustrate the model with the simulations on Dusseldorf network showing how information is spread in several ATIS scenarios (VMS, radio news, online sources, and simultaneous sources).
Journal of Advanced Transportation | 2017
Rafał Kucharski; Arkadiusz Drabicki
This paper proposes a new method to estimate the macroscopic volume delay function (VDF) from the point speed-flow measures. Contrary to typical VDF estimation methods it allows estimating speeds also for hypercritical traffic conditions, when both speeds and flow drop due to congestion (high density of traffic flow). We employ the well-known hydrodynamic relation of fundamental diagram to derive the so-called quasi-density from measured time-mean speeds and flows. This allows formulating the VDF estimation problem with a speed being monotonically decreasing function of quasi-density with a shape resembling the typical VDF like BPR. This way we can use the actually observed speeds and propose the macroscopic VDF realistically reproducing actual speeds also for hypercritical conditions. The proposed method is illustrated with half-year measurements from the induction loop system in city of Warsaw, which measured traffic flows and instantaneous speeds of over 5 million vehicles. Although the proposed method does not overcome the fundamental limitations of static macroscopic traffic models, which cannot represent dynamic traffic phenomena like queue, spillback, wave propagation, capacity drop, and so forth, we managed to improve the VDF goodness-of-fit from of 27% to 72% most importantly also for hypercritical conditions. Thanks to this traffic congestion in macroscopic traffic models can be reproduced more realistically in line with empirical observations.
intelligent tutoring systems | 2015
Rafał Kucharski; Guido Gentile
This paper shows how rerouting phenomena can be observed from the available data and how to derive valuable input to estimate the rerouting models. By rerouting we mean changing the currently chosen path in road network after either receiving some information or observing consequences of an unexpected traffic event. Recently we have proposed Information Comply Model (ICM) to address the rerouting phenomena in Dynamic Traffic Assignment (DTA) [1]. In this paper we focus on estimation framework for the model and verify the assumptions on the rerouting behavior. The paper identifies two datasets where rerouting can be observed: (1) direct - path trajectories; (2) indirect - traffic flows over the cut-set of the network. Proposed method of formal analysis derives from the data the input to estimate the rerouting behavior, namely flow: information spreads (speed and range), drivers observe (how an atypical delay leads to rerouting), drivers decide (utility of rerouting used in discrete-choice model). Indirect estimation method from traffic flows is illustrated with a field-data from Warsaw bridges observed over several consecutive days including day of the event. Central findings are: a) about 20% of the affected traffic flow reroutes, b) rerouting flows are increasing in time, c) drivers show strategic capabilities, d) and maximize their utility while rerouting. Which were assumed while conceiving the ICM model.
Archives of Transport | 2015
Rafał Kucharski; Guido Gentile
In this paper we propose how available dataset can be used to estimate rerouting phenomena in traffic networks. We show how to look at set of paths observed during unexpected events to understand the rerouting phenomena. We use the information comply model [1] and propose its estimation method. We propose the likelihood formula and show how the theoretical and observed rerouting probabilities can be obtained. We conclude with illustrative example showing how a single observed path can be processes and what information it provides. Contrary to parallel paper [2] where rerouting phenomena is estimated using real traffic flow measures from Warsaw, here we use only synthetic data. The paper is organized as follows. First we elaborate on rerouting phenomena and define the traffic network, then we summarize the literature behind rerouting phenomena. We follow with a synthetic definition of dynamic traffic assignment needed to introduce ICM model in subsequent section. Based on that introduction we define the observations and propose estimation method based on them followed by illustrative example. Paper is summarized with conclusions and pointing of future directions.
Scientific And Technical Conference Transport Systems Theory And Practice | 2018
Rafał Kucharski; Tomasz Kulpa; Justyna Mielczarek; Arkadiusz Drabicki
In this paper we propose a new approach to model regional travel demand with a four-step model. Based on a comprehensive travel survey results for the Malopolska region in Poland (over 3mln inhabitants) we analysed the demand model with specific focus on regional trips. As a result, we introduce a modified model structure where long-distance trips are exposed. We breakdown the demand to four demand strata of various destination types to improve regional demand model quality. Such decomposition allows better representation of regional travel demand as compared to the classical four-step approach.
ieee international conference on models and technologies for intelligent transportation systems | 2017
Rafał Kucharski; Bojan Kostic; Guido Gentile
In this paper we revisit the real-time traffic forecasting problem. We review recently proposed Dynamic Traffic Assignment (DTA) methods and verify how they can improve the practice of traffic forecasting. In particular, we analyze: 1) the Gradient projection DTA model of Gentile (2016), 2) Day-to-day model by Watling and Cantarella (2016), 3) the Marginal Computation (MaC) method by Corthout et al. (2014), 4) dynamic origin-destination (O-D) demand estimation methods (Kostic and Gentile, 2015) and 5) the event rerouting model (Kucharski and Gentile, 2014). We discuss how these methods can be applied to improve short-term forecasting and, most importantly, if they are efficient and mature enough for practical, real-time implementations. We formulate the real-time DTA forecasting problem which searches for the solution using all of the above DTA methods. The main contribution of this paper can be seen as a review and synthesis of recently proposed DTA methods, summarized with conceptual real-time forecasting framework.
Archives of Transport | 2017
Arkadiusz Drabicki; Rafał Kucharski; Andrzej Szarata
The objective of this paper is to discuss the replication of passenger congestion (overcrowding) effects on output path choices in public transport assignment models. Based on a comprehensive literature review, the impact of passenger overcrowding effects was summarised in 3 main categories: the inclusion of physical capacity constraints (limits); the feedback effect between transport demand and supply performance; and the feedback effect on travel cost (discomfort penalty). Further on, sample case studies are presented, which prove that the inclusion of capacity constraints might significantly influence the assignment output and overall results in public transport projects’ assessment – yet most state-of-the-practice assignment models would either miss or neglect these overcrowding-induced phenomena. In a classical 4-step demand model, their impact on passengers’ travelling strategies is often limited to path (route) choice stage, while in reality they also have far-reaching implications for modal choices, temporal choices and long-term demand adaptation processes. This notion has been investigated in numerous research works, leading to different assignment approaches to account for impact of public transport capacity constraints – a simplified, implicit approach (implemented in macroscopic-based models, e.g. PTV VISUM), and a more complex, explicit approach (incorporated in mesoscopic-based models, e.g. BusMezzo). In the simulation part of this paper, sample tests performed on a small-scale network aim to provide a general comparison between these two approaches and arising differences in the assignment output. The implicit approach reveals some differences in assignment output once network capacity constraints are accounted for – though in a simplified manner, and producing somewhat ambiguous output (e.g. in higher congestion scenarios). The explicit approach provides a more accurate representation of overcrowding-induced phenomena especially the evolving demand-supply interactions in the event of arising congestion in the public transport network. Further studies should involve tests on a city-scale, multimodal transport model, as well as empirical model validation, in order to fully assess the effectiveness of these distinct assignment approaches.
Archives of Transport | 2015
Rafał Kucharski; Guido Gentile
ieee international conference on models and technologies for intelligent transportation systems | 2017
Arkadiusz Drabicki; Rafał Kucharski; Oded Cats; Achille Fonzone
Archive | 2016
Tomasz Kulpa; Rafał Kucharski; Andrzej Szarata