Including congestion effects in urban road traffic CO2 emissions modelling: Do Local Government Authorities have the right options?

Tailpipe emissions from vehicles on urban road networks have damaging impacts, with the problem exacerbated by the common occurrence of congestion. This article focuses on carbon dioxide because it is the largest constituent of road traffic greenhouse gas emissions. Local Government Authorities (LGAs) are typically responsible for facilitating mitigation of these emissions, and critical to this task is the ability to assess the impact of transport interventions on road traffic emissions for a whole network.This article presents a contemporary review of literature concerning road traffic data and its use by LGAs in emissions models (EMs). Emphasis on the practicalities of using data readily available to LGAs to estimate network level emissions and inform effective policy is a relatively new research area, and this article summarises achievements so far. Results of the literature review indicate that readily available data are aggregated at traffic level rather than disaggregated at individual vehicle level. Hence, a hypothesis is put forward that optimal EM complexity is one using traffic variables as inputs, allowing LGAs to capture the influence of congestion whilst avoiding the complexity of detailed EMs that estimate emissions at vehicle level.Existing methodologies for estimating network emissions based on traffic variables typically have limitations. Conclusions are that LGAs do not necessarily have the right options, and that more research in this domain is required, both to quantify accuracy and to further develop EMs that explicitly include congestion, whilst remaining within LGA resource constraints.     

Greenhouse gas emissions from road construction: An assessment of alternative staging approaches

The greenhouse gas (GHG) emissions associated with road construction activities are analyzed. The main focus of this analysis is on the vehicle emissions associated with alternative project staging approaches, specifically a full closure of the road during construction, versus an intermittent road closure. The analysis includes the direct and upstream emissions associated with materials, construction equipment, mobilization of resources to the work site, and maintenance activity associated with the project over its lifetime. The analysis is based on one case study of a road project in New Jersey. The assumptions underlying the staging analysis are based on hypothetical approaches. Results provide an assessment of the main sources of project related emissions and the ability to minimize total project emissions by minimizing traffic disruption. In the analysis with a full closure of the road, traffic disruption accounts for 26% of total emissions, while with an intermittent road closure, traffic disruption accounts for only 2% of total emissions. The other main sources are from materials and life-cycle maintenance. The analysis demonstrates the feasibility of minimizing project related GHG emissions during road construction activities.     

Validating and improving public transport origin–destination estimation algorithm using smart card fare data

Smart card data are increasingly used for transit network planning, passengers’ behaviour analysis and network demand forecasting. Public transport origin–destination (O–D) estimation is a significant product of processing smart card data. In recent years, various O–D estimation methods using the trip-chaining approach have attracted much attention from both researchers and practitioners. However, the validity of these estimation methods has not been extensively investigated. This is mainly because these datasets usually lack data about passengers’ alighting, as passengers are often required to tap their smart cards only when boarding a public transport service. Thus, this paper has two main objectives. First, the paper reports on the implementation and validation of the existing O–D estimation method using the unique smart card dataset of the South-East Queensland public transport network which includes data on both boarding stops and alighting stops. Second, the paper improves the O–D estimation algorithm and empirically examines these improvements, relying on this unique dataset. The evaluation of the last destination assumption of the trip-chaining method shows a significant negative impact on the matching results of the differences between actual boarding/alighting times and the public transport schedules. The proposed changes to the algorithm improve the average distance between the actual and estimated alighting stops, as this distance is reduced from 806 m using the original algorithm to 530 m after applying the suggested improvements.     

Integrated Transport System of the South-Moravian Region and its impact on rural development

It is generally presupposed that the infrastructure and availability of services of general interest (like schools, medical care, social services and also public transport) impact on present demographic development in rural areas, namely depopulation and aging. Such services affect the quality of life of local people and sometimes they perform a vital necessity. It is possible to say that the absence of the mentioned services should be compensated by an effective system of public transport. In other case, especially those people who are not able to use individual cars due to the age, health, legal conditions or financial situation are bequeathed on an assistance of the family or neighbors or they stay cut off and excluded. This paper is aimed at the verification of the presupposition in the case of the South-Moravian Region – NUTS 3 region occupying the south-eastern part of the Czech Republic, bordering with Austria and Slovakia. The research method lies in analysis of the frequency, travel time and fare of public transport system and its comparison with demographic development in rural areas, especially in the peripheral ones. The results are discussed in view of the system of central places in the region and present urbanization processes like suburbanization, counterurbanization and reurbanization.     

Happy or liberal? Making sense of behavior in transport policy design

Appropriate microeconomic foundations of mobility are decisive for successful policy design in transportation and, in particular, for the challenge of climate change mitigation. Recent research suggests that behavior in transportation cannot be adequately represented by the standard approach of revealed preferences. Moreover, mobility choices are influenced by factors widely regarded as normatively irrelevant. Here we draw on insights from behavioral economics, psychology and welfare theory to examine how transport users make mobility decisions and when it is desirable to modify them through policy interventions. First, we explore systematically which preferences, heuristics and decision processes are relevant for mobility-specific behavior, such as mode choice. We highlight the influence of infrastructure on the formation of travel preferences. Second, we argue that the behavioral account of decision-making requires policy-makers to take a position on whether transport policies should be justified by appealing to preference satisfaction or to raising subjective well-being. This distinction matters because of the (i) influence of infrastructure on preference formation, (ii) health benefits from non-motorized mobility, (iii) negative impact of commuting on happiness and (iv) status-seeking behavior of individuals. The orthodox approach of only internalizing externalities is insufficient because it does not allow for the evaluation of these effects. Instead, our analysis suggests that transport demand modeling should consider behavioral effects explicitly.     

Technical and environmental effects of biodiesel use in local public transport

Biodiesel use in local public transport could be especially significant in improving air quality in cities. The purpose of the experiments described in this paper was to evaluate the various (10, 20 and 50%) blends of biodiesel with diesel in the context of the engine and pollution aspects. As regards the experimental use of these findings on municipal buses, these experiments were the first reference in Hungary. The ages (15–20 years) and types of buses (Ikarus-280, Ikarus-260) used in the experiments are still common vehicles in Hungarian public transport. During our measurements, there was a significant difference between the change in fuel consumption of articulated and solo buses in traffic when compared to test bench measurements. The proportion of the engine performance reduction is nearly the same as that for biodiesel share in the blends. Most pollutants were decreasing (both at idle and full rpm), but this reduction is not directly proportional to the increase of the blending percentage. However, as for CO₂, emission increase was observed in the case of idle rpm in comparison to normal diesel operation, even though this phenomenon was not due to biodiesel use, but the catalytic converter and the fact that biodiesel was used for the first time in the engine concerned.     

Generating lane-based intersection maps from crowdsourcing big trace data

Lane-based road information plays a critical role in transportation systems, a lane-based intersection map is the most important component in a detailed road map of the transportation infrastructure. Researchers have developed various algorithms to detect the spatial layout of intersections based on sensor data such as high-definition images/videos, laser point cloud data, and GPS traces, which can recognize intersections and road segments; however, most approaches do not automatically generate Lane-based Intersection Maps (LIMs). The objective of our study is to generate LIMs automatically from crowdsourced big trace data using a multi-hierarchy feature extraction strategy. The LIM automatic generation method proposed in this paper consists of the initial recognition of road intersections, intersection layout detection, and lane-based intersection map-generation. The initial recognition process identifies intersection and non-intersection areas using spatial clustering algorithms based on the similarity of angle and distance. The intersection layout is composed of exit and entry points, obtained by combining trajectory integration algorithms and turn rules at road intersections. The LIM generation step is finally derived from the intersection layout detection results and lane-based road information, based on geometric matching algorithms. The effectiveness of our proposed LIM generation method is demonstrated using crowdsourced vehicle traces. Additional comparisons and analysis are also conducted to confirm recognition results. Experiments show that the proposed method saves time and facilitates LIM refinement from crowdsourced traces more efficiently than methods based on other types of sensor data.     

An ontology-based framework to support performance monitoring in public transport systems

Managers of public transport systems have been facing for years the strategic challenge of maintaining high quality of transport services to improve the mobility of citizens, while reducing costs and ensuring safety and low environmental impact. A well-established way to evaluate the performance achieved by the system or by specific activities is to monitor Key Performance Indicators (KPI). However, existing management systems, which refer to flexible yet large and complex data models, provide a limited support to define and select relevant KPIs for the objectives at hand, and even the identification of whether and how the data model is capable to achieve a certain informative need is a critical and time-consuming task. This work is aimed to propose a framework to ease the development of a monitoring system in the public transport domain. The approach is based on the ontological representation of all the knowledge regarding indicators and their formulas, business objectives, dimension analysis and their relation with the Transmodel, the European reference data model for public transport information systems. On its top, a reasoning framework provides logic functionalities to interactively support designers in a set of common design tasks: the choice of the most suitable indicators for the performance monitoring needs at hand, the definition of new indicators and the identification of the minimal set of Transmodel modules needed to calculate them. A case study is included to discuss these applications, while an evaluation shows the feasibility of the approach.     

A traffic-condition-based route guidance strategy for a single destination road network

Most of existing route guidance strategies achieves user optimal equilibrium by comparing travel time. Measuring travel time, however, might be uneasy on an urban road network. To contend with the issue, the paper mainly considers easily obtained inflow and outflow of a link and road capacity as input, and proposes a route guidance strategy for a single destination road network based on the determination of free-flow or congested conditions on alternative routes. An extended strategy for a complex network and a feedback approximation for avoiding forecast are further explored. Weaknesses of the strategy are also explicitly analyzed. To test the strategy, simulation investigations are conducted on two networks with multiple parallel routes. The results indicate that the strategy is able to provide stable splitting rates and to approximate user optimal equilibrium in different conditions, in particular when traffic demand is high. This strategy has potential to be applied in an urban road network due to its simplicity and easily obtained input data. The strategy is also applicable for single destination if some alternatives and similar routes are available.