Characterization of VOCs from LPG and unleaded petroleum fuelled passenger cars

The study monitored the emissions of volatile organic compounds (VOCs) from the exhaust of cars fuelled by liquefied petroleum gas (LPG) and unleaded petrol (ULP). Six cars, four fuelled by LPG and two by ULP, were tested on a chassis dynamometer at two different cruising modes of operation (60 km h−1 and 80 km h−1) and idle. A total of 33 VOCs were identified in the exhaust of both types of fuels by the use of GC/MS. Due to the complexity of the dataset, Multi Criteria Decision Making (MCDM) software PROMETHEE and GAIA was used to rank the least polluting mode and fuel. The 60 km h−1 driving speed was identified as the cleaner mode of driving as was LPG fuel. The Ozone Formation Potential (OFP) of the VOCs was also calculated by using the incremental reactivity scale. Priority VOCs leading to ozone formation were identified according to the three incremental reactivity scales: MIR, MOIR and EBIR. PROMETHEE was applied to assess the most preferred scale of reactivity for predicting ozone formation potential under different scenarios. The results enhance the understanding of the environmental value of using LPG to power passenger cars.

TrainYarn: Probing perceptions of social space in urban commuter trains

We studied the ways that urban commuter train passengers experience their journeys. We present the design process and in-situ evaluation of TrainYarn, a mobile app prototype designed to facilitate social interaction between co-located urban train passengers. Through the deployment of the prototype, we sought to probe perceptions of social space with a view to positively impact the assessment of public transport. Our results support that our target users saw value in the use of TrainYarn, perceiving it as emancipatory, in alignment with their communicative needs, and having the ability to transform their perceptions of social space. To further inform future research and practice, we put forward a series of design recommendations.

Questionnaire design for airport passenger experience survey

Assessing airport service performance requires understanding of a complete set of passenger experiences covering all activities from departures to arrivals. Weight-based indicator models allow passengers to express their priority on certain evaluation criteria (airport domains) and their service attributes over the others. The application of multilevel regression analysis in questionnaire design is expected to overcome limitations of traditional questionnaires, which require application of all indicators with equal weight. The development of a Taxonomy of Passenger Activities (TOPA), which captures all passenger processing and discretionary activities, has provided a novel perspective in understanding passenger experience in various airport domains. Based on further literature reviews on various service attributes at airport passenger terminals, this paper constitutes questionnaire design to employ a weighting method for all activities from the time passengers enter an airport domain at the departure terminal until leaving the arrival terminal (i.e. seven airport domains for departure, four airport domains during transit, and seven airport domains for arrival). The procedure of multilevel regression analysis is aimed not only at identifying the ranking of each evaluation criterion from the most important to the least important but also to explain the relationship between service attributes in each airport domain and overall service performance.

Validation of a 4DOF manoeuvring model of a high-speed vehicle-passenger trimaran

This paper presents the validation of a manoeuvring model for a novel 127m-vehicle-passenger trimaran via full scale trials. The adopted structure of the model is based on a model previously proposed in the literature with some simplifications. The structure of the model is discussed. Then initial parameter estimates are computed, and the final set of parameters are obtained via adjustments based on engineering judgement and application of a genetic algorithm so as to match the data of the trials. The validity of the model is also assessed with data from a trial different from the one use for the parameter adjustment. The model shows good agreement with the trial data.

Assessment of transit quality of service with occupancy load factor and passenger travel time measures

This paper investigates quality of service (QoS) and resource productivity implications of transit route passenger loading and travel time. It highlights the value of occupancy load factor as a direct passenger comfort QoS measure. Automatic Fare Collection data for a premium radial bus route in Brisbane, Australia, is used to investigate time series correlation between occupancy load factor and passenger average travel time. Correlation is strong across the entire span of service in both directions. Passengers tend to be making longer, peak direction commuter trips under significantly less comfortable conditions than off-peak. The Transit Capacity and Quality of Service Manual uses segment based load factor as a measure of onboard loading comfort QoS. This paper provides additional insight into QoS by relating the two route based dimensions of occupancy load factor and passenger average travel time together in a two dimensional format, both from the passenger’s and operator’s perspectives. Future research will apply Value of Time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent onboard. This would also assist in transit service quality econometric modeling. The methodology can be readily applied in a practical setting where AFC data for fixed scheduled routes is available. The study outcomes also provide valuable research and development directions.

Rail fatigue and the role of impact forces

Fatigue of the steel in rails continues to be of major concern to heavy haul track owners despite careful selection and maintenance of rails. The persistence of fatigue is due in part to the erroneous assumption that the maximum loads on, and stresses in, the rails are predictable. Recent analysis of extensive wheel impact detector data from a number of heavy haul tracks has shown that the most damaging forces are in fact randomly distributed with time and location and can be much greater than generally expected. Large- scale Monte-Carlo simulations have been used to identify rail stresses caused by actual, measured distributions of wheel-rail forces on heavy haul tracks. The simulations show that fatigue failure of the rail foot can occur in situations which would be overlooked by traditional analyses. The most serious of these situations are those where track is accessed by multiple operators and in situations where there is a mix of heavy haul, general freight and/or passenger traffic. The least serious are those where the track is carrying single-operator-owned heavy haul unit trains. The paper shows how using the nominal maximum axle load of passing traffic, which is the key issue in traditional analyses, is insufficient and must be augmented with consideration of important operational factors. Ignoring such factors can be costly.

Analyzing multiple spike trains with nonparametric granger causality

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons limultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.

Measuring route passenger load diversity for capacity and quality of service assessment

This paper develops theory that quantifies transit route passenger-relative load factor and distinguishes it from occupancy load factor. The ratio between these measures is defined as the load diversity coefficient, which as a single measure characterizes the diversity of passenger load factor between route segments according to the origin-destination profile. The relationship between load diversity coefficient and route coefficient of variation in occupancy load factor is quantified. Two tables are provided that enhance passenger capacity and quality of service (QoS) assessment regarding onboard passenger load. The first expresses the transit operator’s perspective of load diversity and the passengers’ perspective of load factor relative to the operator’s, across six service levels corresponding to ranges of coefficient of variation in occupancy load factor. The second interprets the relationships between passenger average travel time and each of passenger-relative load factor and occupancy load factor. The application of this methodology is illustrated using a case study of a premium radial bus route in Brisbane, Australia. The methodology can assist in benchmarking and decision making regarding route and schedule design. Future research will apply value of time to QoS measurement, reflecting perceived passenger comfort through crowding and average time spent aboard. This would also assist in transit service quality econometric modeling.

Measuring route passenger load diversity for capacity and quality of service assessment

This poster introduces Passenger Relative Load Factor for a route or individual bus service as a capacity and quality of service measure, distinguishing it from Occupancy Load Factor. It introduces Load Diversity Coefficient as the ratio of Passenger Relative Load Factor to Occupancy Load Factor, and relates Load Diversity Coefficient to Coefficient of Variation in Occupancy Load Factor. It qualifies the operator’s and passengers’ perspectives of load factor based on Coefficient of Variation in Occupancy Load Factor along a route. A case study using weekday Automatic Fare Collection (AFC) data on a premium bus line in Brisbane, Australia illustrates the methodology. The compendium paper also qualifies the operator’s and passengers’ perspectives of these load factors along with Passengers’ Average Travel Time for capacity and quality of service assessment.

Statistical significance of sequential firing patterns in multi-neuronal spike trains

Sequential firings with fixed time delays are frequently observed in simultaneous recordings from multiple neurons. Such temporal patterns are potentially indicative of underlying microcircuits and it is important to know when a repeatedly occurring pattern is statistically significant. These sequences are typically identified through correlation counts. In this paper we present a method for assessing the significance of such correlations. We specify the null hypothesis in terms of a bound on the conditional probabilities that characterize the influence of one neuron on another. This method of testing significance is more general than the currently available methods since under our null hypothesis we do not assume that the spiking processes of different neurons are independent. The structure of our null hypothesis also allows us to rank order the detected patterns. We demonstrate our method on simulated spike trains.

Conditional Probability-Based Significance Tests for Sequential Patterns in Multineuronal Spike Trains

We consider the problem of detecting statistically significant sequential patterns in multineuronal spike trains. These patterns are characterized by ordered sequences of spikes from different neurons with specific delays between spikes. We have previously proposed a data-mining scheme to efficiently discover such patterns, which occur often enough in the data. Here we propose a method to determine the statistical significance of such repeating patterns. The novelty of our approach is that we use a compound null hypothesis that not only includes models of independent neurons but also models where neurons have weak dependencies. The strength of interaction among the neurons is represented in terms of certain pair-wise conditional probabilities. We specify our null hypothesis by putting an upper bound on all such conditional probabilities. We construct a probabilistic model that captures the counting process and use this to derive a test of significance for rejecting such a compound null hypothesis. The structure of our null hypothesis also allows us to rank-order different significant patterns. We illustrate the effectiveness of our approach using spike trains generated with a simulator.