Many cities have ambitious climate targets, like becoming climate-neutral by 2050, 2040 or 2030, but are uncertain about how mobility and land use should change in order to reduce GHG emissions to levels consistent with the cities’ aims. Finding answers on this, and also on the question of how to strike an effective balance between desired fundamental transformation and realistic expectations, was at the heart of the Interreg Europe project 2050 CliMobCity (2050 Climate-friendly Mobility in Cities). The project partners were four medium-sized but otherwise quite different cities (Bydgoszcz, Plymouth, Thessaloniki and Leipzig), along with the Potsdam Institute for Climate Impact Research (PIK) and TU Delft. Each partner city conducted a so-called demonstration study, which required defining ambitious packages of measures (mobility, land use, electrification) and forecasting changes in mobility by macroscopic transport modelling. PIK then analysed GHG emissions using its carbon model. This paper extends the demonstration studies into a case-study approach. This is preceded by an exercise in consistently structuring types of GHG and mobility performances and corresponding measures. The case-study approach is supplemented by a review of the literature on shared and micromobility. A conclusion drawn from the case study is that none of the partner cities is in fact sufficiently reducing GHG emissions. Even if electricity production was completely green, the remaining gap between GHG reduction aims and analysed GHG delivery lies between 30–81 %, dependent on the scenario and city. Shared and micromobility seem not to lead to strongly deviating conclusions. We discuss policies to close the gap. One major option discussed is tackling GHG emissions from urban freight, in forms such as organising public-private cooperation designed to accelerate the electrification of freight vehicles.

A common problem in public transport systems is bus bunching, characterized by a negative feedback loop between service headways, number of boarding passengers and dwell times. In this study, we examine whether providing real-time crowding information (RTCI) at the stop regarding the two next vehicle departures can stimulate passengers to wait for a less-crowded departure, and thus alleviate the bunching effect. To this end, we leverage on results from own stated-preference survey and develop a boarding choice model. The model accounts for the presence of RTCI and is implemented within dynamic public transport simulation framework. Application to the case-study model of a major bus corridor in Warsaw (Poland) reveals that RTCI can induce a significant probability (30–70%) of intentionally skipping an overcrowded bus and waiting for a later departure instead. This behaviour, in turn, results in significantly lower vehicle headway and load variations, without deteriorations in total waiting utility. Overall, journey experience improves by 6%, and crucially—the prevalence of denial-of-boarding and excessive on-board overcrowding is substantially reduced, by ca. 40%. Results of our study indicate that the willingness to wait induced by RTCI can be a potential demand management strategy in counteracting bunching, with benefits already attainable at limited RTCI response rates.

Bus bunching is a well-known problem in public transport networks. It is characterized by a self-amplifying relationship between uneven distribution of rising passenger loads and deteriorating service regularity. The focus of this study is to analyse whether this negative feedback loop can be addressed by providing real-time crowding information (RTCI) on next vehicle departures at stops. We integrate a departure choice model based on stated-preference analysis of passengers’ willingness to wait with RTCI. A proof-of-concept application to a toy-network model shows that this prevents further progression of bunching effects in certain demand conditions. The RTCI usage reveals substantial benefits - in terms of relative reductions in on-board (over)crowding, headway deviations, as well as mitigated denial-of-boarding risk - in moderately saturated network. These gains may diminish though as high overcrowding eventually emerges in PT network. Nevertheless, our findings indicate that RTCI has the potential to improve travel experience and service utilisation efficiency, even without resorting to supply-side control strategies.

The purpose of this study would be to analyse the implications of providing real-time crowding information (RTCI) on instantaneous (current) crowding levels of public transport (PT) services to travelers – and more specifically, to investigate the output RTCI impact for the on-going PT system performance, based on simulation works on a case study (city-level) network model. In recent years, an increasing emphasis has been put on development of various travel demand management tools and especially advanced traveler information systems (ATIS), which aim to overcome problems associated with ever growing passenger congestion in urban transport systems - and thus improve the overall travel experience, reliability and quality of service of the PT system. One of key ATIS developments has witnessed the widespread introduction of real-time information (RTI) systems [3], which provide passengers with information on current travel times of public transport services, waiting times at stops etc. Likewise, a further extension of these systems is highly feasible within the framework of modern-day ITS systems, as passenger flow data collected from various sources – APC and AFC systems, smart-card ticketing systems etc. – can be then utilized to inform travelers about the current passenger flows, i.e. real-time loading levels of public transport vehicles (the so-called RTCI system). However, this also indicates a substantial research gap as (to the best of our knowledge) there is still a fairly limited amount of relevant studies, with just individual sources having investigated the effects of RTCI provision in terms of simulation approach [2], [5] or survey analysis [6], [9]. Moreover, practical implementation of such systems in PT networks is yet (to this date) confined to limited-scale deployment, often on pilot (trial) basis. Consequently, little is known about the potential effectiveness of RTCI systems and their implications both on demand (passengers’) side or supply (operators’ side).

The objective of this paper is to understand the consequences of providing real-time information on crowding levels (RTI-CL) in public transport networks. We propose to extend the mesoscopic, simulation-based assignment model with the passengers' knowledge of instantaneous crowding levels onboard the public transport vehicles. We illustrate the results on a sample transit network, where we investigate the arising changes in network performance and journey experience as a result of RTI-CL provision. We demonstrate that effects of providing the crowding information in real-time on en-route path choices is strongly related to number of assumptions that we address in the paper. The resulting changes are dependent on: network congestion level (the effects are mostly pronounced in moderately congested networks), passengers' behaviour (increased sensitivity to crowding induces higher variability in vehicle loads), the RTI-CL penetration rate (network performance becomes worse with ubiquitous access to information) and information provision type (smoothed information over recent vehicle runs leads to higher accuracy than instantaneous information based on the single latest run only). Based on these, we formulate conclusions for further studies, larger-scale applications and practical implementation of real-time passenger crowding information systems.