Exploring the capacity of social media data for modelling travel behaviour: Opportunities and challenges

In the past few years, the social science literature has shown significance attention to extracting information from social media to track and analyse human movements. In this paper the transportation aspect of social media is investigated and reviewed. A detailed discussion is provided about how social media data from different sources can be used to indirectly and with minimal cost extract travel attributes such as trip purpose, mode of transport, activity duration and destination choice, as well as land use variables such as home, job and school location and socio-demographic attributes including gender, age and income. The evolution of the field of transport and travel behaviour around applications of social media over the last few years is studied. Further, this paper presents results of a qualitative survey from travel demand modelling experts around the world on applicability of social media data for modelling daily travel behaviour. The result of the survey reveals positive view of the experts about usefulness of such data sources.     

Multi-period planning of closed-loop supply chain with carbon policies under uncertainty

Climate change and greenhouse gases emissions have caused countries to implement various carbon regulatory mechanisms in some industrial sectors around the globe to curb carbon emissions. One effective method to reduce industry environmental footprint is the use of a closed-loop supply chain (CLSC). The decision concerning the design and planning of an optimal network of the CLSC plays a vital role in determining the total carbon footprint across the supply chain and also the total cost. In this context, this research proposes an optimization model for design and planning a multi-period, multi-product CLSC with carbon footprint consideration under two different uncertainties. The demand and returns uncertainties are considered by means of multiple scenarios and uncertainty of carbon emissions due to supply chain related activities are considered by means of bounded box set and solve using robust optimization approach. The model extends further to investigate the impact of different carbon policies such as including strict carbon cap, carbon tax, carbon cap-and-trade, and carbon offset on the supply chain strategic and operational decisions. The model captures trade-offs that exist among supply chain total cost and carbon emissions. Also, the proposed model optimizes both supply chain total cost and carbon emissions across the supply chain activities. The numerical results reveal some insightful observations with respect to CLSC strategic design decisions and carbon emissions under various carbon policies and at the end we highlighted some managerial insights.     

Decomposing passenger transport futures: Comparing results of global integrated assessment models

The transport sector is growing fast in terms of energy use and accompanying greenhouse gas emissions. Integrated assessment models (IAMs) are used widely to analyze energy system transitions over a decadal time frame to help inform and evaluating international climate policy. As part of this, IAMs also explore pathways of decarbonizing the transport sector. This study quantifies the contribution of changes in activity growth, modal structure, energy intensity and fuel mix to the projected passenger transport carbon emission pathways. The Laspeyres index decomposition method is used to compare results across models and scenarios, and against historical transport trends. Broadly-speaking the models show similar trends, projecting continuous transport activity growth, reduced energy intensity and in some cases modal shift to carbon-intensive modes - similar to those observed historically in a business-as-usual scenario. In policy-induced mitigation scenarios further enhancements of energy efficiency and fuel switching is seen, showing a clear break with historical trends. Reduced activity growth and modal shift (towards less carbon-intensive modes) only have a limited contribution to emission reduction. Measures that could induce such changes could possibly complement the aggressive, technology switch required in the current scenarios to reach internationally agreed climate targets.     

Equilibrium scheduling of vehicle-to-grid technology using activity based modelling

Vehicle-to-grid (V2G) is a technology which can reduce the cost for power distribution network operators by storing electricity in the batteries of electric-drive vehicles and retrieving it when energy demands increase during the course of a day. Participants of V2G are reimbursed for offering their vehicles which can lead to changes in trip schedules when V2G payments are high and travelers are sensitive to the payments. However, prior studies have ignored the effects of V2G on travelers’ schedules. This research gap is addressed with a bi-level V2G market equilibrium model where the lower level model determines the equilibrium activity patterns as a result of upper level pricing and linear approximated AC flow distribution decisions. An algorithm is proposed for the model and illustrated on a simple telecommuting example where travelers can work from home and offer their vehicle charge capacity to the power provider. The model is then applied to the same case study from Lam and Yin (2001) to first replicate the lower level equilibrium problem as a special case when no V2G is present, and then to show the potential effects of the V2G policy to decrease locational marginal prices for a distribution network operator. The proposed algorithm for the V2G policy resulted in a substantial 20% increase in social welfare over the benchmark equilibrium without V2G.     

Contrasting the direct use of data from traffic radars and video-cameras with traffic simulation in the estimation of road emissions and PM hotspot analysis

This study investigates the effect of traffic volume and speed data on the simulation of vehicle emissions and hotspot analysis. Data from a microwave radar and video cameras were first used directly for emission modelling. They were then used as input to a traffic simulation model whereby vehicle drive cycles were extracted to estimate emissions. To reach this objective, hourly traffic data were collected from three periods including morning peak (6–9 am), midday (11–2 pm), and afternoon peak (3–6 pm) on a weekday (June 23, 2016) along a high-volume corridor in Toronto, Canada. Traffic volumes were detected by a single radar and two video cameras operated by the Southern Ontario Centre for Atmospheric Aerosol Research. Traffic volume and composition derived from the radar had lower accuracy than the video camera data and the radar performance varied by lane exhibiting poorer performance in the remote lanes. Radar speeds collected at a single point on the corridor had higher variability than simulated traffic speeds, and average speeds were closer after model calibration. Traffic emissions of nitrogen oxides (NOx) and particulate matter (PM₁₀ and PM_2.5) were estimated using radar data as well as using simulated traffic based on various speed aggregation methods. Our results illustrate the range of emission estimates (NO_x: 4.0–27.0 g; PM₁₀: 0.3–4.8 g; PM_2.5: 0.2–1.3 g) for the corridor. The estimates based on radar speeds were at least three times lower than emissions derived from simulated vehicle trajectories. Finally, the PM₁₀ and PM_2.5 near-road concentrations derived from emissions based on simulated speeds were two or three times higher than concentrations based on emissions derived using radar data. Our findings are relevant for project-level emission inventories and PM hot-spot analysis; caution must be exercised when using raw radar data for emission modeling purposes.     

Influence of composition,amount and life span of passenger cars on end-of-life vehicles waste in Belgium: A system dynamics approach

The increasing worldwide production of passenger cars depletes natural resources and increases the number of end-of-life vehicles (ELVs) to be processed. Lack of landfill capacity and a growing scarcity of natural resources have lead to severe ELV reuse and recovery targets in the European Union (EU). This paper examines the main influencing parameters affecting the amount and composition of ELV waste originating from passenger cars to be treated in Authorized Treatment Facilities (ATFs). Moreover the effect of a changing number, composition and life span of passenger cars on the ability to meet the ambitious EU ELV Directive 2000/53/EC targets in 2015 is examined for Belgium. Using system dynamics, the aforementioned changing parameters are studied from 1990 and projected to 2030. The model results show that the total annual weight of ELV waste to be reused and recycled in Belgium is expected to grow over the coming years despite the economic downturn of 2008 and its effect on GDP growth. Moreover it shows that Belgium can sustainably achieve the ambitious EU ELV Directive 2000/53/EC targets in 2015 and thereafter if it continues to invest in waste treatment of ELV plastics. The availability of higher amounts of ELV plastics proves to be favourable to trigger investments in their reusing and recycling. We demonstrate that this can be realized by reducing export of discarded passenger cars, shortening the life span of passenger cars or shortening the time for investing in additional plastic recovery.     

Network traffic control based on a mesoscopic dynamic flow model

The paper focuses on Network Traffic Control based on aggregate traffic flow variables, aiming at signal settings which are consistent with within-day traffic flow dynamics. The proposed optimisation strategy is based on two successive steps: the first step refers to each single junction optimisation (green timings), the second to network coordination (offsets). Both of the optimisation problems are solved through meta-heuristic algorithms: the optimisation of green timings is carried out through a multi-criteria Genetic Algorithm whereas offset optimisation is achieved with the mono-criterion Hill Climbing algorithm. To guarantee proper queuing and spillback simulation, an advanced mesoscopic traffic flow model is embedded within the network optimisation method. The adopted mesoscopic traffic flow model also includes link horizontal queue modelling. The results attained through the proposed optimisation framework are compared with those obtained through benchmark tools.     

波浪潮流作用下的悬沙输移

本文提出了波浪潮流作用下悬沙输移的二维数值模拟,并讨论了模型中的几个重要参数,模拟结果与天津港区现场情况很吻合。     

Calibrating aggregate travel demand models with traffic counts: Estimators and statistical performance

Traffic counts on network links constitute an information source on travel demand which is easy to collect, cheap and repeatable. Many models proposed in recent years deal with the use of traffic counts to estimate Origin/Destination (O/D) trip matrices under different assumptions on the type of "a-priori" information available on the demand (surveys, outdated estimates, models, etc.) and the type of network and assignment mapping (see Cascetta & Nguyen 1988). Less attention has been paid to the possibility of using traffic counts to estimate the parameters of demand models. In this case most of the proposed methods are relative to particular demand model structures (e.g. gravity-type) and the statistical analysis of estimator performance is not thoroughly carried out. In this paper a general statistical framework defining Maximum Likelihood, Non Linear Generalized Least Squares (NGLS) and Bayes estimators of aggregated demand model parameters combining counts-based information with other sources (sample or a priori estimates) is proposed first, thus extending and generalizing previous work by the authors (Cascetta & Russo 1992). Subsequently a solution algorithm of the projected-gradient type is proposed for the NGLS estimator given its convenient theoretical and computational properties. The algorithm is based on a combination of analytical/numerical derivates in order to make the estimator applicable to general demand models. Statistical performances of the proposed estimators are evaluated on a small test network through a Monte Carlo method by repeatedly sampling "starting estimates" of the (known) parameters of a generation/distribution/modal split/assignment system of models. Tests were carried out assuming different levels of "quality" of starting estimates and numbers of available counts. Finally NGLS estimator was applied to the calibration of the described model system on the network of a real medium-size Italian town using real counts with very satisfactory results in terms of both parameter values and counted flows reproduction.     

Pollution history of a tropical estuary revealed by combined hydrodynamic modelling and sediment geochemistry

Hydrodynamic modelling of water movement in Hunts Bay, a protected part of Kingston Harbour, Jamaica, shows that depth averaged tidal flows are very low. In the northeast corner of Hunts Bay, water is essentially stagnant. Even under high flow conditions, much of the Bay bottom water is ‘bypassed' by buoyant, lower salinity surface flows. The muddy sediments of Hunts Bay reflect these sluggish to stagnant conditions; sediment cores from the northeast corner of the Bay contain progressively higher amounts of organic matter in their upper parts (last 15–20 years sedimentation). Combined C/N ratios and stable carbon isotope compositions of this organic matter imply a sewage origin. Both lead and chromium metal concentrations and enrichment factors relative to average crustal shales show geographically related patterns that reflect hydrodynamic circulation predicted by modelling. In particular, metal concentrations and enrichment factors are highest at the northern end of the bay, especially in the northeast corner. Modelling confirms that stagnant conditions would occur in the northeast part of the bay even without the presence of a major causeway. The causeway may contribute to low flow conditions, but is not the principal cause of organic contamination, which is simply an excessive input of sewage.