title: Performance evaluation of SCATS-controlled intersections in New Zealand with machine-learning delay prediction and signal-timing  optimisation

authors: Khatri, Shivu

abstract: Adaptive signal systems such as the Sydney Coordinated Adaptive Traffic System (SCATS) 
controls traffic signal system by adjusting phase splits and cycle length in real time. The 
adjustments rely only on the last few seconds of detector data, the system lacks real-time predictive 
and optimization functionalities, allowing queues and emissions to build. Fine-tuning of signal 
timing by even a small increment can derive large economic and environmental benefits for the 
wider network. This short-coming is becoming increasingly significant in New Zealand, where 
transport already contributes 39 % of national CO₂ emissions and intersection delay costs 
Auckland more than NZ $1 billion each year.
This thesis evaluates present and future performance of two representative SCATS-controlled 
intersections—Albany, Auckland and Ruakura, Hamilton—and tests whether supervised machine-
learning (ML) models can predict delay and recommend cycle lengths that improvises the native 
SCATS logic.
Field data was used to build, calibrate, and validate base SIDRA intersection models; saturation-
flow rates were matched within ±5 % of observations and all lane-movements satisfied degree-of-
saturation criteria. A Monte-Carlo routine expanded 14 SCATS peak-period volume logs into 98 
synthetic volume scenarios, which were re-run in SIDRA to obtain delay, queue, fuel, and emission 
outputs. Four machine learning models—XGBoost, Random Forest, Support Vector Regression, 
and k-Nearest Neighbours—were trained on the synthetic dataset; the best two, XGBoost and 
Random Forest were combined in an ensemble to give a delay-prediction model. 
Furthermore, baseline analysis (2024) found both sites operating at Level-of-Service D, with 
several right-turn lanes already oversaturated and 95th-percentile queues exceeding storage. Ten-
year growth projections degraded both intersections to LoS F well before the planning horizon. 
The ML ensemble predicted average control delay with MAE ≈ 4 s veh⁻¹ and, under moderate 
demand, shortened cycles by 15–46 s, cutting delay by 13 % and CO₂ by 5 % relative to SCATS
timings; though the benefits diminished under heavy oversaturation.
Data-driven machine learning models can provide cycle-by-cycle delay forecasts and substantial
performance gains, but require additional high-degree of saturations and longer cycle lengths
training data for robust operation and more generalization during severe congestion. Integration of 
such predictors and optimizers with SCATS would provide practical step toward meeting New 
Zealand’s 2035 emission-reduction targets.

text: Performance evaluation of SCATS-controlled intersections in New Zealand with machine-learning delay prediction and signal-timing  optimisation Adaptive signal systems such as the Sydney Coordinated Adaptive Traffic System (SCATS) 
controls traffic signal system by adjusting phase splits and cycle length in real time. The 
adjustments rely only on the last few seconds of detector data, the system lacks real-time predictive 
and optimization functionalities, allowing queues and emissions to build. Fine-tuning of signal 
timing by even a small increment can derive large economic and environmental benefits for the 
wider network. This short-coming is becoming increasingly significant in New Zealand, where 
transport already contributes 39 % of national CO₂ emissions and intersection delay costs 
Auckland more than NZ $1 billion each year.
This thesis evaluates present and future performance of two representative SCATS-controlled 
intersections—Albany, Auckland and Ruakura, Hamilton—and tests whether supervised machine-
learning (ML) models can predict delay and recommend cycle lengths that improvises the native 
SCATS logic.
Field data was used to build, calibrate, and validate base SIDRA intersection models; saturation-
flow rates were matched within ±5 % of observations and all lane-movements satisfied degree-of-
saturation criteria. A Monte-Carlo routine expanded 14 SCATS peak-period volume logs into 98 
synthetic volume scenarios, which were re-run in SIDRA to obtain delay, queue, fuel, and emission 
outputs. Four machine learning models—XGBoost, Random Forest, Support Vector Regression, 
and k-Nearest Neighbours—were trained on the synthetic dataset; the best two, XGBoost and 
Random Forest were combined in an ensemble to give a delay-prediction model. 
Furthermore, baseline analysis (2024) found both sites operating at Level-of-Service D, with 
several right-turn lanes already oversaturated and 95th-percentile queues exceeding storage. Ten-
year growth projections degraded both intersections to LoS F well before the planning horizon. 
The ML ensemble predicted average control delay with MAE ≈ 4 s veh⁻¹ and, under moderate 
demand, shortened cycles by 15–46 s, cutting delay by 13 % and CO₂ by 5 % relative to SCATS
timings; though the benefits diminished under heavy oversaturation.
Data-driven machine learning models can provide cycle-by-cycle delay forecasts and substantial
performance gains, but require additional high-degree of saturations and longer cycle lengths
training data for robust operation and more generalization during severe congestion. Integration of 
such predictors and optimizers with SCATS would provide practical step toward meeting New 
Zealand’s 2035 emission-reduction targets.

year: 2025

title: Third–order nulling effect in Darlington transistors

authors: Balsom, Toby

subjects: engineering

abstract: In this paper we present the first proof that a Darlington transistor has an inherent nulling effect in its third–order intermodulation distortion, similar to the well–known third-order null seen in single BJT amplifiers. It is proven mathematically and by measurement. The results suggest the null actually becomes feasible as a source of distortion reduction in a Darlington BJT amplifier.

text: Third–order nulling effect in Darlington transistors In this paper we present the first proof that a Darlington transistor has an inherent nulling effect in its third–order intermodulation distortion, similar to the well–known third-order null seen in single BJT amplifiers. It is proven mathematically and by measurement. The results suggest the null actually becomes feasible as a source of distortion reduction in a Darlington BJT amplifier.

year: 2011

title: Modelling loanword success a sociolinguistic quantitative study of Māori loanwords in New Zealand English

authors: Calude, Andreea S.

subjects: loanwords

abstract: Loanword use has dominated the literature on language contact and its salient nature continues to draw interest from linguists and non-linguists. Traditionally, loanwords were investigated by means of raw frequencies, which are at best uninformative and at worst misleading. Following a new wave of studies which look at loans from a quantitatively more informed standpoint, modelling “success” by taking into account frequency of the counterparts avail- able in the language adopting the loanwords, we propose a similar model of loan-use and demonstrate its benefits in a case study of loanwords from Māori into (New Zealand) English. Our model contributes to previous work in this area by combining both the success measure mentioned above with a rich range of linguistic characteristics of the loanwords (such as loan length and word class), as well as a similarly detailed group of sociolinguistic characteristics of the speakers using them (gender, age and ethnicity of both, speakers and addresses). Our model is unique in bringing together of all these factors at the same time. The findings presented here illustrate the benefit of a quantitatively balanced approach to modelling loanword use. Furthermore, they illustrate the complex interaction between linguistic and sociolinguistic factors in such lan- guage contact scenarios.

text: Modelling loanword success a sociolinguistic quantitative study of Māori loanwords in New Zealand English Loanword use has dominated the literature on language contact and its salient nature continues to draw interest from linguists and non-linguists. Traditionally, loanwords were investigated by means of raw frequencies, which are at best uninformative and at worst misleading. Following a new wave of studies which look at loans from a quantitatively more informed standpoint, modelling “success” by taking into account frequency of the counterparts avail- able in the language adopting the loanwords, we propose a similar model of loan-use and demonstrate its benefits in a case study of loanwords from Māori into (New Zealand) English. Our model contributes to previous work in this area by combining both the success measure mentioned above with a rich range of linguistic characteristics of the loanwords (such as loan length and word class), as well as a similarly detailed group of sociolinguistic characteristics of the speakers using them (gender, age and ethnicity of both, speakers and addresses). Our model is unique in bringing together of all these factors at the same time. The findings presented here illustrate the benefit of a quantitatively balanced approach to modelling loanword use. Furthermore, they illustrate the complex interaction between linguistic and sociolinguistic factors in such lan- guage contact scenarios.

year: 2017